Datadog, Inc. — Call Transcript 2026

I would now like to turn the conference over to Yuka Broderick, Senior Vice President of Investor Relations. Please go ahead. Thank you, Lisa. Good morning, and thank you all for joining us to review Datadog's first quarter 2026 financial results, which we announced in our press release issued this morning. Joining me on the call today are Olivier Pomel, Datadog's Co-founder and CEO, and David Obstler, Datadog CFO. During this call, we will make forward-looking statements, including statements related to our future financial performance, our outlook for the second quarter and the fiscal year 2026 and related notes and assumptions, our product capabilities, and our ability to capitalize on market opportunities. The words anticipate, believe, continue, estimate, expect, intend, will, and similar expressions are intended to identify forward-looking statements and similar indications of future expectations. These statements reflect our views today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially. For a discussion of the material risks and other important factors that could affect our actual results, please refer to our Form 10-K for the year ended December 31, 2025. Additional information will be made available on our upcoming Form 10-Q for the fiscal quarter ending March 31, 2026 and other filings with the SEC. This information is also available on the investor relations section of our website, along with a replay of this call. We will discuss non-GAAP financial measures, which are reconciled to their most directly comparable GAAP financial measures in the tables in our earnings release, which is available at investors.datadoghq.com. With that, I'd like to turn the call over to Olivier. Thank you, Yuka, and thank you all for joining us to go over a very strong start to 2026. Let me begin with this quarter's business drivers. I'm very pleased to say that our teams executed very well and delivered revenue growth of 32% year-over-year, accelerating from 29% last quarter and 25% in the year-ago quarter. We showed broad-based acceleration of revenue growth across cohorts, including both our AI and non-AI customers. Our AI-native customers cohort continue to grow and diversify rapidly, both in the number of customers we serve and the scale of those customers. This quarter included new land deals with two of the world's biggest AI research teams, helping them improve and optimize their training workflows. I'll talk more about that in a bit. Even more impressive was the growth in our non-AI customers. Non-AI customer revenue growth accelerated again this quarter to mid-20s% year-over-year, up from 23% last quarter and 19% in the year-ago quarter. We think this is a sign of strong continued cloud migration, greater adoption of our products, and customers of all kinds accelerating their use of AI. Finally, churn has remained low, with gross revenue retention stable in the mid-to-high 90s, highlighting the mission-critical nature of our platform for our customers. Regarding our Q1 financial performance and key metrics, revenue was $1.1 billion, an increase of 32% year-over-year and above the high end of our guidance range. We ended Q1 with about 33,200 customers, up from about 30,500 a year ago. We also ended with about 4,550 customers with an ARR of $100,000 or more, up from about 3,770 a year ago. These customers generated about 90% of our ARR. We generated free cash flow of $289 million, with a free cash flow margin of 29%. Turning to product adoption, our platform strategy continues to resonate in the market. For example, 56% of our customers now use four or more products, up from 51% a year ago. 35% of our customers use six or more products, up from 28% a year ago, and 20% of our customers use eight or more products, up from 13% a year ago. We're landing more customers and delivering value across more products, and our business continues to grow. Our total ARR now exceeds $4 billion, and our quarterly revenue exceeded $1 billion for the first time in Q1. This is a big achievement for all of us at Datadog and is a product of years of investment in building and innovating for our customers. We're still just getting started. Of our 26 products, five are over $100 million in ARR, and another three are between $50 million-$100 million in ARR. We're working hard to build and deliver further growth in those products. This leaves 18 other products which are earlier in their life cycles. We believe each has the potential to grow to more than $100 million over time. Moving on to R&D. Our engineers, enabled with the latest AI coding tools, are building rapidly to help our customers confidently and securely deploy their applications. Let me speak to a few of our product launches this quarter. Let's start with AI. As a reminder, we're talking about our AI efforts in two buckets, AI for Datadog and Datadog for AI. First, AI for Datadog. These are AI products and capabilities that make the Datadog platform better and more useful for our customers. In March, we launched our MCP Server for general availability. With MCP Server, developers access live production data to debug their applications directly in their AI coding agent or IDE. We delivered Bits AI Security Analyst, which autonomously triages Datadog Cloud SIEM signals, conducts in-depth investigations of potential threats, and delivers actionable recommendations. We've seen Bits AI Security Analyst reduce investigations that could take hours to as little as 30 seconds. We also shipped Bits Assistant, now in preview, which helps customers search and act across Datadog using natural language prompts. Moving on to Datadog for AI. This includes Datadog capabilities that deliver end-to-end observability and security across the AI stack. We launched GPU Monitoring, enabling teams to understand GPU fleet utilization, workload efficiency, thermal and power behavior, and interconnect performance. This drives higher GPU ROI and operational reliability. Our customers continue to move forward with their AI activities, and we can see that in their usage of the Datadog platform. We now have over 6,500 customers sending data for one or more of our AI integrations. Though this is only 20% of total customers, they represent about 80% of our ARR. Our customers' usage of AI within Datadog platform continues to grow rapidly. Bits AI SRE agent investigations have more than doubled from December to March. The number of spans sent to our LLM Observability product nearly tripled quarter-over-quarter. The number of Datadog MCP Server tool calls quadrupled quarter-over-quarter, and the number of Bits Assistant messages increased by a factor of 12 in that period. While we are aggressively building with and for AI, we also continue to expand the Datadog platform to deliver against our customers' increasingly complex needs. To speak to a few of these efforts, last month, we launched Experiments for general availability. Experiments work hand in hand with our feature flagging product and combine best-in-class statistical methods with real-time observability guardrails so companies can test for impact, choose among alternatives quickly, and ship with confidence. In addition, our customers now benefit from APM Recommendations. By analyzing telemetry data from application performance monitoring, user monitoring, profiler, and database monitoring, APM Recommendations automatically identify performance and reliability issues, and most importantly, explain how to fix them. We announced our plans to launch our next data center in the U.K. We see a large opportunity to serve our British customers as cloud adoption accelerates in regulated industries. Last but not least, we are pleased to have received FedRAMP High certification from the U.S. federal government. With this certification, we can now move forward with federal agency customers that require FedRAMP High to handle sensitive workloads. Meanwhile, we continue to expand our product offerings, go-to-market teams, and channel partnerships for public sector customers, both in the U.S. and internationally. Our teams were hard at work again, and we're looking forward to sharing many new products and feature announcements at our DASH user conference on June 9th and 10th in New York City. Let's move on to sales and marketing and highlight some of the deals we closed this quarter. First, we landed two large deals, a seven-figure and an eight-figure annualized deals with the AI research divisions at two of the world's largest technology companies. These organizations are building and training the most advanced AI models in the world. It is critical for them to reduce engineering friction and increase training velocity. Fragmented internal and open source tooling made it harder to identify and solve issues and reduce engineering and research productivity. By using Datadog, both companies are accelerating their pace of innovation on their hyperscale AI training workloads. This includes optimizing their workflows using GPU Monitoring on large parallel GPU grids. Next, we signed a seven-figure annualized expansion for an eight-figure annualized deal with a leading online recruiting platform. This customer is centralizing on Datadog to reduce complexity, drive developer velocity, and improve efficiency. With this expansion, they will replace a standalone tool with Datadog LLM Observability to correlate LLM signals with APM and user experience data. This customer will grow to 16 Datadog products, including Datadog MCP Server. We signed a seven-figure annualized expansion for an eight-figure annualized deal with a Fortune 500 bank. With this expansion, this customer will migrate their remaining log data into Datadog, fully replacing their legacy log vendor. Most notably, our Flex Logs give them granular control over costs while meeting strict compliance requirements. This customer uses 10 Datadog products, including Bits AI SRE Agent, to accelerate incident response with AI. We signed a seven-figure annualized expansion with a leading global hedge fund. This customer operates thousands of on-prem hosts and network devices. At that scale, their open source monitoring stack has become operationally unsustainable, impacting portfolio managers and investment analysts. With this expansion, they will replace their entire on-prem observability layer with Datadog Infrastructure Monitoring and Network Device Monitoring. It will have unified visibility across their cloud and on-prem environments. This customer will expand to 11 Datadog products. We landed a six-figure annualized deal with a Fortune 500 insurance company. This company's fragmented observability stack led to long outages with incident supported first by their customers instead of their tooling. By using Datadog and consolidating three legacy APM tools, they expect to move from reactive responses to proactive incident detection. They will adopt 10 Datadog products to start, including all three pillars and LLM Observability. Next, we signed a seven-figure annualized expansion with one of the world's largest travel groups in APAC. This customer was using Datadog on one business unit, but in two others, they were juggling multiple tools and lacked actionable insights. By consolidating six legacy open source and cloud monitoring tools, the customers save money and improve platform resiliency and performance. This multi-year commitment positions Datadog as their strategic observability provider. Finally, we landed a six-figure annualized deal with a leading Latin American fintech company. This customer serves tens of millions of users across critical financial flows. Their rapid growth outpaced their fragmented front-end monitoring setup, and outages exposed them to financial, operational, and reputational risks. By adopting our Digital Experience Monitoring suite, including RUM, Synthetics, and Product Analytics, they now have full visibility over user activities with the cost control they also previously lacked. This customer will start with 5 Datadog products. That's it for our wins. Congratulations again to our entire go-to-market organization for a great Q1. Before I turn it over to David for a financial review, I want to say a few words on our longer term outlook. We are pleased with the way we started 2026 as we support our customers' inflection in AI usage and application development, and as they lean into our AI innovations, including Bits AI SRE, Bits AI Security Analyst, Bits Assistant, Datadog MCP Server, GPU Monitoring, and many more. There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business. We now have an additional secular growth driver with AI as we help our customers deliver more value with this transformative new technology. Now more than ever, we feel ideally positioned to help customers of every size and every industry, as well as all type of users, whether humans or AI agents, so they can transform, innovate, and drive value through AI and cloud adoption. With that, I will turn it over to our CFO, David. Thanks, Olivier. This was a very strong quarter for Datadog. Our Q1 revenue was $1.01 billion, up 32% year-over-year. Our 6% quarter-over-quarter revenue growth is the highest for a Q1 since 2022, and our $53 million quarter-over-quarter revenue added is the highest ever for a Q1. That included the strongest quarter of sequential usage growth from existing customers since the first quarter of 2022. We also delivered an all-time record for sequential ARR added to the quarter. ARR growth accelerated in each month of Q1, and we see a continuation of these healthy growth trends in April. We also achieved strong new logo bookings. New logo annualized bookings set a new all-time record by a significant margin and more than double versus a year ago quarter. These included wins in observability and included some of our newer products like security, Data Observability, and Flex Logs. Our new logo average land size also set a record and more than doubled year-over-year as we continue to land larger deals. Revenue growth accelerated with our broad base of customers, excluding the AI natives, to mid-20s percent year-over-year, up from 23% last quarter and 19% in the year-ago quarter. We saw robust growth across our customer base with broad-based strength across customer size, spending bands, and industries. Meanwhile, our AI native customer growth continues to significantly outpace the rest of the business. This group continues to diversify and grow, including 22 customers spending more than $1 million annually and five spending more than $10 million annually. This group includes the leading companies in foundational models, code-gen tools, and vertical specific AI solutions. Next, regarding our retention metrics. Our trailing-12-month net revenue retention percentage was in the low 120%, up from about 120 last quarter, and our trailing-12-month gross retention percentage remains in the mid to high 90s. Now moving on to our financial results. Billings were $1.03 billion, up 37% year-over-year, and remaining performance obligations, or RPO, was $3.48 billion, up 51% year-over-year, with current RPO growing in the mid-40s percent year-over-year. RPO duration increased year-over-year as the mix of multi-year deals increased in Q1. As a reminder, we continue to believe revenue is a better indicator of our business trends than billings and RPO, given their variability. Now let's review some of the key income statement results. Unless otherwise noted, all metrics are non-GAAP, and we have provided a reconciliation of GAAP to non-GAAP financials in our earnings release. First, Q1 gross profit was $807 million with a gross margin of 80.2%. This compares to a gross margin of 81.4% last quarter and 80.3% in the year-ago quarter. As we've discussed in the past, our gross margin varies from quarter-to-quarter, with investments into innovations for our customers offset by efficiency efforts. Our Q1 OpEx grew 31% year-over-year versus 29% last quarter and 29% in the year-ago quarter. As a reminder, we continue to grow our investments to pursue our long-term growth opportunities, and this OpEx growth is an indication of our execution of our hiring plans. Q1 operating income was $223 million for a 22% operating margin, compared to 24% last quarter and 22% in the year ago quarter. Turning to the balance sheet and cash flow statements. We ended the quarter with $4.8 billion in cash equivalents and marketable securities. Our cash flow from operations was $335 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $289 million, and free cash flow margin was 29%. Now for our outlook for the second quarter and for the fiscal year 2026. First, our guidance philosophy overall remains unchanged. As a reminder, we base our guidance on trends observed in recent months and apply conservativism on these growth trends. In addition, as with last quarter, we are applying a higher degree of conservativism to our largest customer. For the second quarter, we expect revenues to be in the range of $1.07 billion-$1.08 billion, which represents a 29%-31% year-over-year growth. This guidance implies sequential revenue growth of $64 million-$74 million, or 6%-7%, due to the strong growth of revenue in Q1 and into April. Non-GAAP operating income is expected to be in the range of $225 million-$235 million, which implies an operating margin of 21%-22%. As a reminder, in Q2 we will be holding our Dash User Conference, which we estimate to cost about $15 million and which we have reflected in our operating income guidance. Non-GAAP net income per share is expected to be $0.57-$0.59 per share based on approximately 369 million weighted average diluted shares outstanding. For fiscal 2026, we expect revenues to be in the range of $4.3 billion-$4.34 billion, which represents 25%-27% year-over-year growth. Non-GAAP operating income is expected to be in the range of $940 million-$980 million, which implies an operating margin of 22%-23%. Non-GAAP net income per share is expected to be in the range of $2.36-$2.44 per share based on approximately 372 million weighted average diluted shares outstanding. Finally, some additional notes on the guidance. We expect net interest and other income for fiscal 2026 to be approximately $170 million. We expect cash taxes for 2026 to be approximately $30 million-$40 million. We continue to apply a 21% non-GAAP tax rate for 2026 and going forward. We expect capital expenditures and capitalized software together to be 4%-5% of revenue in fiscal 2026. To summarize, we are very pleased with our execution in Q1. We are well-positioned to help our existing and prospective customers with their cloud migration, digital transformation, and AI adoption journeys. I want to thank Datadogs worldwide for their efforts. With that, we'll open the call for questions. Operator, let's begin the Q&A. Thanks. Thank you. As a reminder, if you would like to ask a question, please press star one one on your telephone. You'll hear the automated message advising your hand is raised. We also ask that you please wait for your name and company to be announced before proceeding with your question. One moment while we compile the Q&A roster. Our first question today is coming from the line of Mark Murphy of JPMorgan. Please go ahead. Thank you so much, and congratulations on an amazing performance. Olivier, is there any way to conceptualize the growth in the sheer raw volume of code that's being produced in the world today due to adoption of code generators such as Claude Code and Codex and Cursor because they seem to be developing the capability to take on full projects. As some of the charts are showing, these capabilities are just exponentially exploding upward in a straight line. I'm wondering how much of that code is going into production and, therefore driving activity for Datadog. Well, we definitely think and see that there's many more applications being created. There's gonna be way more complexity in production. We see some of that happening already today. Some of those new applications are getting into production. They are finding users. We see some signs of that at every layer of our platform. You know, we quoted a few stats on the increasing data volumes we see in our AI products. That's definitely a reflection of that. We see an inflection point there in consumption from customers. We see a move to production that is very real, and we see that across both AI native and non-AI companies. Okay. Thank you. As just a quick related follow-up. If we click down one layer, you know, I'm wondering how you might view the increasing heterogeneity of the environments at the silicon level. Because when you look across Amazon with Trainium and Graviton, Google with TPUs, Microsoft has launched the Maia silicon, it looks like that is starting to explode. You know, our understanding is that trying to monitor the mixed environments is a lot more difficult than if you just have a uniform fleet of Intel and AMD chips. We keep hearing all the traditional monitoring tools, because they really fail on the custom silicon and Datadog handles it well. All this new telemetry, including high bandwidth memory and that type of thing, could you speak to whether that trend is giving you some tailwind? Yeah. I mean, look, the broader market that's interesting here is. You know, training used to be something only two or three companies were doing or maybe four or five, at a large scale. It looks like training actually might democratize quite a bit more, and many companies will train models on a regular basis. It becomes more of a viable category for service provider like us, basically. I think the heterogeneity of the silicon is definitely a trend that plays in our favor there. You know, the more heterogeneous, the more you need someone else to make sense of everything for you and tie it all together and also tie it all with the non-GPU aspects and the rest of the infrastructure and the application and the users and the developers, like, basically everything we do for you. There's only You know, when you think of who actually has heterogeneous environments today, that is still a very small number of companies. You know, Google barely just started selling their TPUs to the outside. You know, I think it's still a small number of companies out that are there, but we see a growing opportunity there. Interestingly, you know, last year when we reported earnings, we said we're mostly interested in inference workloads and training is not really a market for us yet. Now we actually see training becoming a market. We started landing customers that are actually hyperscalers that have a whole host of homegrown technologies, and that are using us specifically in their super intelligence labs to help monitor their workloads, accelerate the training runs, monitor the GPUs also. We see that as a point of validation that there's a, there's gonna be a good market for us there. Well, that's amazing to think there's a whole new dimension where if you can move from inferencing into the training side. I caught the reference in the prepared remarks of how you landed a couple of those very large labs. Congrats on everything. Thank you for taking my questions. Thank you. One moment for the next question. Our next question will be coming from the line of Sanjit Singh of Morgan Stanley. Your line is open. Yeah. Thank you for taking the question. I want to start it off with David. You know, this guide to start the year is probably the best we've seen in several years, David, and you laid out the underlying assumptions quite well. Just wanted to do a sanity gut check just on the sort of overall macro backdrop. We do have some geopolitical tensions and those types of things when we think about your mid-SMBs business and any impact from like in your, you know, e-commerce or retail business where there may be some, you know, consumer discretionary impacts. I just want to get, like, how you're thinking about those parts of the business, and then I had a follow-up for Ollie. Yeah. We had a very strong quarter across the board. We had, you know, multi-industry, multi-geography type of quarter. SMB was very strong. You know that the source of our, our guidance and our raises are at the, at the core, that type of performance. We haven't seen any particular effect in the consumer businesses or e-commerce businesses yet. We basically have a continuation of trends in those businesses, travels and things like that are very similar to, you know, the other industries. We haven't seen it yet. We obviously watch it and look at analytics, but we haven't seen it. In terms of our overall guidance. You know, the trends that we have in organic, we discount across the board, and I think we mentioned our particular treatment of our largest customer. No, that's very clear. Olivier, for you, I think when we talk to investors about the debate in this category longer term, it's just what does this, what does the category look like when agents are doing the triaging, investigating versus human engineers and human SREs? What is your sort of vision of that, how that evolves for Datadog, both from a product standpoint and an experience standpoint, from a UI perspective. Also, like, is there going to be new modalities in terms of pricing when agents are consuming the Datadog platform to a higher degree than engineers do today? Yeah. Look, I think one thing I'd say is it's hard to tell where we're gonna be in four or five years. You know, if you had told me two years ago that most engineers would go back to coding in the console, I would not believe you. Yet, you know, that's one of the winning modalities today. Look, as far as we're concerned, we don't care whether most of the usage is humans, most of the usage is agents. Our business model lends itself to it pretty well. Like, we're usage-based, it doesn't really matter where the usage is coming from that perspective. The way we see trends sum up right now is we see both a stratospheric increase of agent usage. We have a ton of usage on our MCP Server. We see customers trying to automate a lot with their own agents, using our agents, using a combination of those. We also see an increase of usage of the web interfaces by humans. Right now, the two work hand in hand, and we keep developing and pushing on both fronts. Appreciate the thoughts. Thank you. Thank you. One moment for the next question. Next question is coming from the line of Raimo Lenschow of Barclays. Please go ahead. Hey, thanks, and congrats from me as well. One for Olivier and for David. Yeah. If I listen to you and to your prepared remarks, there's a lot of, like, consolidation that people try to do open source tooling and then realize they kind of needed to come to you and come back. On the other hand, in the industry, we still have a lot of, like, noise around that level. You know, how do you see it in real life? To me, it seems a little bit like observability is just very hot and then, you know, there's different categories where you use certain vendors and some open source tooling. Can you speak what you see in real life there? Thank you. I mean, in real life, you know, most companies have open source in some capacity somewhere. When it comes to having a platform that, you know, unifies everything, takes care of everything, does more of the problem-solving for you, that's, that, you know, that's typically why customers use us, you know. The motion we see pretty much, you know, everywhere is customers have four, six, seven, 15, 25 different things and different pockets in the organizations and different business units, and it's a, it's a huge mess. They come to us so they can unify all that. They get better results because all of the data is in one place. The workflows can be automated from end-to-end. You can get end-to-end visibility. You don't have blind spots. Also, they save money because they don't have all these pockets in inefficiency everywhere. It's a win, you know, for everyone. The thing that's also interesting in particular this quarter is that we also landed some large parts of hyperscalers. Hyperscalers typically have a culture of building everything themselves. You know, they certainly have the balance sheet and the human capital to support, you know, some of that build-out. Like, if there was ever a set of companies for whom it makes sense to do it themselves, that would be those companies. Yet, you know, we see that they have the same issues. You know, when it comes to going as fast as they can, being as efficient as they can with their resources, like, they come to us to replace some of the things they were using before. Two things, two metrics to look at that to make the points, Oli, you're making. If you look at our platform adoption and you see both the growth of the different categories and the extension of the categories out to lots of pro-products, that shows you that the consolidation on the Datadog platform, you know, has continued and is a very strong trend. Part of that is the movement of solutions, as Oli had mentioned, that are both open source, but also the competitive point solutions onto the platform. That's been a significant driver of the revenue growth for some time now, and that continued certainly in Q1. Okay. Perfect. Thank you. David, for you, the last year, we did a lot of investments around go-to-market, especially on, in sales capacity. If you think about now the non-AI category doing better, how much of that is people doing the cloud migrations again, so that's an industry trend? How much of that is you guys actually being broader positioned? Thank you. Yeah. Well, it's a number of things, including, one is the expansion of the platform, the successful ramping of sales capacity, while not jeopardizing productivity, which has resulted in increasing ARR and, you know, a good environment as well. I think that's what we said last time. There are a number of factors, and certainly what we're proving out here is the investments we made in go-to market and are continuing are paying off and, were the right decision. Oli, anything to add? Yeah. Look, at the end of the day, there's clearly some market tailwinds with the adoption of AI. Also we are outperforming all of our competitors at scale, and we're taking share. That relates to the structural platform, the way we expand with new products, the way these products are maturing and starting to win in their respective categories, and the way we've successfully grown the sales capacity. Certainly AI, the AI investment trend has helped, but what we're trying to do is separate that, and AI investment is probably helping the overall as well. When you really take that out, you see a very pronounced acceleration here, and that has to do with the factors that I mentioned and Oli talked about. Perfect. Congrats. Sounds exciting. Thanks. One moment please for the next question. Our next question is coming from the line of Gabriela Borges of Goldman Sachs. Please go ahead. Hi. Good morning. Thank you. Olivier, I find your comments on training versus inference so interesting. Maybe just crystallize for us, why do you think the training opportunity is happening now or inflecting now? And then either for yourself or David, how do we think about the attach rate on training versus inference of observability? If there is a way to benchmark observability spend as a percentage of inference spend, does that number change given the new data that you're seeing on the training side as well? Thank you so much. On the training side, training was very new a couple of years ago. It was something that was only done by very few companies, and it was, in a way, very artisanal. It was not a production workload, it was something that researchers were building, and that was very one-off and homegrown in ways. Now it's turning into production. It's turning into something that many more companies are doing. It's scaling by orders of magnitude, and it's becoming something that has to be on all the time, reliable and, you know, every minute you lose is a or rather every failure you have in your training run is a week you give away to the competition. As a result, you know, it becomes way more interesting as a, as a market for companies like us. We see some signs of that. You know, again, we didn't have a lot of it. We didn't see a lot of it last year. Now all of a sudden we're starting to see quite a bit of activity there and demand, and we have success landing with large customers with those products. Yeah. I think going back to the metrics that Oli talked about, you know, in terms of attach, we said that 6,500 customers are using our integrations, and that's 20% of the customers and 80% of the ARR. There is attach. I think it's earlier days for the training, you know, all that looks like it will be a contributor. I think we- that's early and I would sort of look at the larger attachment at this point as the evidence of inference but also some training. Thank you both, and congratulations. Thank you. Thank you. One moment please for the next question. Our next question is coming from the line of Karl Keirstead of UBS. Please go ahead. Okay, great. I wanted to start, Olivier and David and you congratulating all of you and the team on reaching that billion-dollar milestone. Well done. David, maybe the question is for you and to hone in specifically on the 2Q guide. Even if you put up a modest beat on that guide, it's going to be, you know, by order of magnitude, the largest sequential dollar add, I think, in the company's history. I just wanted to unpack what's giving you that confidence. In particular, is there anything interesting to call out, David, in terms of the ramp of a couple of the larger research labs, one of which renewed with you guys in the fourth quarter, another one just landed. I presume they're ramping nicely in 2Q, but would love any color. Thank you. Yeah. Let me unpack this in a couple ways. As you know, we're a recurring revenue model. The biggest indication of in the near term or the next quarter is the ARR growth in the previous quarter, and when we said we had a record. Essentially, at the bedrock of this is sort of the run forward of ARR that we've already signed. The ARR add was very broad-based and was not very concentrated. Whereas we pointed out some very significant adds, I would say that the first quarter and that ARR add was really diversified and from lots of different places. I think Oli will come in here, but the confidence that we have is, you're right, we essentially take what we already have. We discount the growth trends that we've seen, and that produces what you exactly said, which is, you know, whatever your assumptions are on beat, you know, a very impressive sequential, really due to what happened in Q1 and the rate of business accumulation by Datadog. Oli, do you wanna add? Yeah, I would. I mean, I basically want to develop on what David just said. The adds were broad-based. Why we have a great Q1? We have also landed great customers in Q4. We had talked about it a quarter ago. Even if you take the, if you take out the customer we land in Q4 that added the most revenue in Q1, we still had a record quarter in terms of ARR adds. This is really broad-based. We landed a few more customers in Q1 that don't contribute any revenue yet, but we expect to be big contributors in the future. When you put all that together, we feel very confident about Q2, hence the numbers you're seeing. Yeah. Thank you. Thank you. One moment, please. Our next question will be coming from the line of Fatima Boolani of Citi. Please go ahead. Good morning. Thank you for taking my questions. Oli, I wanted to double back on a question that was asked earlier with respect to telemetry volumes, you know, essentially going parabolic, and you are accessing brand new demand vectors, you know, in the foray into training and monitoring, observing training model environments inside some of the world's largest frontier labs. I wanted to ask you about the structural changes to the capital intensity of the business. I mean, your CapEx levels are still pretty respectable and pretty muted. I wanted to get a better understanding of what sort of extrinsic or intrinsic engineering efforts you're undertaking to keep a very efficient CapEx envelope, in spite of the fact that it seems like that would increase because of the torrent of telemetry you're seeing on the platform. As a related matter, we've seen a rise of sovereign data and data residency requirements kind of ramp as AI models, you know, move into the territory of national security and things like that. Wondering if you can kind of talk to some of the engineering horsepower internally that you're leveraging to be able to keep a really tight command on capital intensity and frankly, your gross margins. Thank you. Yeah. I mean, look, the investments we're making right now, we run most of our workloads on clouds, meaning you'll see all of that in OpEx, not in CapEx. We have low CapEx, you know. If it changes, we'll tell you. Like if for some reason we decide to make different kinds of investments and some of it more up-front, some of it more CapEx, we'll tell you, but that's not the case today. We are definitely ramping up our investments in particular in R&D, and in the scale of the models we train ourselves and things like that. Right now, there's nothing that you can actually see in the numbers that moves any needle, but you know, if that changes, also we'll tell you. We don't expect any change to our model. That's on the CapEx side. We're very different businesses in that way from the AI labs. On the subject of data residency and sovereignty of AI, you know, and things like that, we definitely see more push for that, more demand for that in the customer base. For us, that means investments in two areas. One is in deploying into more geographies and having more certifications to sell to the public sector and to the highest levels of the public sector. You know, we mentioned today data center in the U.K., for example, or our FedRAMP High certification. We're not stopping there, you know, in terms of the certification we're going after with the federal government. That's an area of investment. Another area of investment is our Bring Your Own Cloud products, where we can actually run on our customers' infrastructure. We announced that. We released some products there, and we have heavy investment in that area, so we can support customers that want to operate in a slightly separate way from the rest of our customer base. Thank you. Thank you. One moment for the next question. Our next question is coming from the line of Kirk Materne of Evercore. Please go ahead. Yeah. Thanks very much for taking the question, and congrats on a nice start. Oli, I was wondering if you could just give some thoughts on the idea of sort of security for agents. I think one of the big, you know, issues in terms of getting agents into production, you know, is sort of the security aspect of that. How do you see Datadog plugging into that opportunity? Just a quick one for David. Congrats on the FedRAMP, you know, reaching that milestone. You know, are your partner relationships in place to take advantage of this? I realize it'll be a, you know, long-term opportunity, but just kinda curious how well established you are down there to start seeing some maybe bookings in that area. Thanks, guys. Yeah. On the security of agents, we interface with that in two ways. First, there's the agents we build ourselves because we are building a lot of automation inside of our product for our customers and agents that automatically identify but also resolve issues without you having to do anything. There, a lot of it has to do with understanding what permissions to apply, what kind of guardrails to apply, how to interface with the humans, and you know, how to make that trustworthy and visible in the right way. That's pretty much the whole product surface is figuring that out. The automation itself actually kind of works already. You should expect to hear more about that at our conference. This is definitely one big area of investment for us. The security aspects of running agents. Look, we, our belief in security is that you need to integrate. You can't just have point solutions that look at one sliver of the whole security posture. You need to look at everything all together. That's one of the areas that we are also covering with our security efforts. That's part of the whole platform actually. On the FedRAMP. We've been working on both the different certifications, but at the same time, we've been investing in the go-to-market function, both in terms of reps and channel partners for a number of years. Certainly, there's more investment to be done, but we invested ahead of the certifications because, you know, in this sector, building pipeline, et cetera, takes time. Certainly, the channel partner relationships are a very important part of this, and we have been investing, but also have more investment to do. Thank you. One moment, please, for the next question. Our next question is coming from the line of Patrick Colville of Scotiabank. Please go ahead. Thank you for taking my question and echoing the congrats of my peers. I guess, you know, Olivier and David, you guys are very deliberate in your messaging on the prepared remarks, I just wanna double-check the kind of wording of one of the comments. I think, David, you said higher degree of conservatism to the largest customer. I guess, did I hear that right? Does the higher degree of conservatism reference versus the other customer cohorts, or does it reference versus your guidance philosophy in prior quarters vis-à-vis this customer? It's both. It's the same guidance we used. We're being very explicit. For all the business except for this largest customers, we've always taken the drivers and discounted them. For this particular customer, we took a higher degree of conservatism than the other part of the customer base and discounted it more. We were, I think, in the remarks, you interpreted correct, very explicit, and you're correct. I would, you know, give that much weight to the very specific way that we're deliberate, but not all that deliberate, you know. Similarly, both David and I have a raspy voice today, but there's no hidden meaning. I will remind everybody that we did not change. If the question also, I think you asked, is did we change or is this a different methodology of both the overall and the large customer than the guidance the last quarter or the previous? The answer is no. It's the same methodology, and that we've had. No change, but that's been what we've always been doing. Okay, and Olivier, can I ask about your comments about the hyperscalers? 'Cause I thought that was particularly interesting. The reason why is I don't think you called them out previously before, and, you know, they are so prevalent in the modern tech stack. To your point, they could do this themselves. I guess how are they using Datadog? Is it for more kind of traditional observability, or is it for these newer areas like GPU Monitoring that Datadog has performed so well in, you know, of late? Well, it's both actually. When you look in general at the large AI customers, they use Datadog the way other companies are largely with a fairly broad set of our products to cover the full surface of observability. What's new is we now have a product for GPU Monitoring. It's a very new product. We see the hyperscalers that are coming to us for training workloads in particular, being very interested in that. Again, it's too early in the product life cycle and the customer life cycle for these specific customers to call definitive victory there. We see that as a very encouraging sign of where the market might go in the future because we think this might be a bellwether of what, you know, the next 100, 500 companies that are going to start training workloads are going to want to do. We have some signs that go beyond the customers we signed this quarter that point that way too. Thank you so much. Thank you. One moment for the next question, please. Our next question is coming from the line of Peter Weed of Bernstein Research. Please go ahead. Thank you. I'll echo others on the momentum. Great to see. You know, one of, I think, the great successes you talked about was landing a couple of the AI labs for the hyperscalers. Although I think, you know, on the other hand, you've talked in the past around, you know, hyperscalers are typically building observability in-house. What is it really about the AI workloads that are making it more attractive for them to use Datadog? What might give you confidence that Datadog might be more persistent with them in these types of workloads and as kind of a signal for maybe how other customers might use Datadog around AI differentiated from things that they might be able to bring in-house other places? Well, you know, the same reason all of our customers use us, you know, it's a high stakes, high complexity, and not core. Like, you know, they have to be always differentiated. They can't afford to be late. It's a really hard job to do all of that. That's what we build our whole business on. It's also very true for at the highest level for the largest companies. Thank you. One moment please. I mean, again. I'm sorry. Oh, yeah. No, I was just gonna say, but I guess, you know, the point is you've emphasized that those largest customers have been able to go in-house on some other things. Is there something- Yeah. ... unique about AI that prevents them from doing that here? Well, I think the urgency of the development efforts focuses the minds. That's what I would put it, you know. I would say, you know, it forces you to figure out what's core and what's not core and what you need to do to maximize your chances of success. Again, it is a same thinking all of our customers have all the time. I think the equation for hyperscalers has often been slightly different because they have, let's call it, unlimited access to staffings. You know, they could sort of set their own time, their own time horizons for the developments they wanted to make. I think the situation is a little bit different with AI race maybe. Thank you. The line of Gregg Moskowitz of Mizuho. Please go ahead. All right. Great. Thank you. I'll add my congratulations on a terrific quarter. Just one for me. Oli, I know it's not GA yet, but curious if you have any early feedback on your new CloudPrem offering. As you noted earlier, you know, providing the ability for Datadog to run on customer infrastructure. Could this be another, yet another, I should say, incremental growth opportunity for Datadog? What are your expectations for this? Well, definitely we think, you know, as I think there was a question earlier on, data residency and, you know, living in customers environment. We definitely see a great opportunity there. You know, there's a chance that a good portion of the market, you know, leans this way in the future. You know, today it's not the largest part of the market, but we definitely see a potential for that. We're investing heavily in that side of our product. We're starting to see some interesting customer traction there, you know. We think this can be another growth lever definitely. We also think that it can help us getting into some extremely large scale workload where customers would not have considered a SaaS offering before, where we can be in the running. That's very exciting. Great. Thank you. All right. I think that was our last question. I want to thank you all for attending the call, and remind you that we have a conference in just a bit more than a month, and I hope to see many of you there. Thank you all. This concludes today's program. You may all disconnect.

Speaker 3: I would now like to turn the conference over to Yuka Broderick, Senior Vice President of Investor Relations. Please go ahead. I would now like to turn the conference over to Yuka Broderick, Senior Vice President of Investor Relations. i would now like to turn the conference over to yuka broderick senior vice president of investor relations Please go ahead. please go ahead

Speaker 4: Thank you, Lisa. Good morning, and thank you all for joining us to review Datadog's first quarter 2026 financial results, which we announced in our press release issued this morning. Joining me on the call today are Olivier Pomel, Datadog's Co-founder and CEO, and David Obstler, Datadog CFO. Thank you, Lisa. thank you lisa Good morning, and thank you all for joining us to review Datadog's first quarter 2026 financial results, which we announced in our press release issued this morning. good morning and thank you all for joining us to review datadog's first quarter 2026 financial results which we announced in our press release issued this morning Joining me on the call today are Olivier Pomel, Datadog's Co-founder and CEO, and David Obstler, Datadog CFO. joining me on the call today are olivier pomel datadog's co-founder and ceo and david obstler datadog cfo During this call, we will make forward-looking statements, including statements related to our future financial performance, our outlook for the second quarter and the fiscal year 2026 and related notes and assumptions, our product capabilities, and our ability to capitalize on market opportunities. The words anticipate, believe, continue, estimate, expect, intend, will, and similar expressions are intended to identify forward-looking statements and similar indications of future expectations. These statements reflect our views today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially. During this call, we will make forward-looking statements, including statements related to our future financial performance, our outlook for the second quarter and the fiscal year 2026 and related notes and assumptions, our product capabilities, and our ability to capitalize on market opportunities. during this call we will make forward-looking statements including statements related to our future financial performance our outlook for the second quarter and the fiscal year 2026 and related notes and assumptions our product capabilities and our ability to capitalize on market opportunities The words anticipate, believe, continue, estimate, expect, intend, will, and similar expressions are intended to identify forward-looking statements and similar indications of future expectations. the words anticipate believe continue estimate expect intend will and similar expressions are intended to identify forward-looking statements and similar indications of future expectations These statements reflect our views today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially. these statements reflect our views today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially For a discussion of the material risks and other important factors that could affect our actual results, please refer to our Form 10-K for the year ended December 31, 2025. Additional information will be made available on our upcoming Form 10-Q for the fiscal quarter ending March 31, 2026 and other filings with the SEC. For a discussion of the material risks and other important factors that could affect our actual results, please refer to our Form 10-K for the year ended December 31, 2025. for a discussion of the material risks and other important factors that could affect our actual results please refer to our form 10-k for the year ended december 31 2025 Additional information will be made available on our upcoming Form 10-Q for the fiscal quarter ending March 31, 2026 and other filings with the SEC. additional information will be made available on our upcoming form 10-q for the fiscal quarter ending march 31 2026 and other filings with the sec This information is also available on the investor relations section of our website, along with a replay of this call. We will discuss non-GAAP financial measures, which are reconciled to their most directly comparable GAAP financial measures in the tables in our earnings release, which is available at investors.datadoghq.com. With that, I'd like to turn the call over to Olivier. This information is also available on the investor relations section of our website, along with a replay of this call. this information is also available on the investor relations section of our website along with a replay of this call We will discuss non-GAAP financial measures, which are reconciled to their most directly comparable GAAP financial measures in the tables in our earnings release, which is available at investors.datadoghq.com. we will discuss non-gaap financial measures which are reconciled to their most directly comparable gaap financial measures in the tables in our earnings release which is available at investors.datadoghq.com With that, I'd like to turn the call over to Olivier. with that i'd like to turn the call over to olivier

Speaker 2: Thank you, Yuka, and thank you all for joining us to go over a very strong start to 2026. Let me begin with this quarter's business drivers. I'm very pleased to say that our teams executed very well and delivered revenue growth of 32% year-over-year, accelerating from 29% last quarter and 25% in the year-ago quarter. We showed broad-based acceleration of revenue growth across cohorts, including both our AI and non-AI customers. Thank you, Yuka, and thank you all for joining us to go over a very strong start to 2026. thank you yuka and thank you all for joining us to go over a very strong start to 2026 Let me begin with this quarter's business drivers. let me begin with this quarter's business drivers I'm very pleased to say that our teams executed very well and delivered revenue growth of 32% year-over-year, accelerating from 29% last quarter and 25% in the year-ago quarter. i'm very pleased to say that our teams executed very well and delivered revenue growth of 32% year-over-year accelerating from 29% last quarter and 25% in the year-ago quarter We showed broad-based acceleration of revenue growth across cohorts, including both our AI and non-AI customers. we showed broad-based acceleration of revenue growth across cohorts including both our ai and non-ai customers Our AI-native customers cohort continue to grow and diversify rapidly, both in the number of customers we serve and the scale of those customers. This quarter included new land deals with two of the world's biggest AI research teams, helping them improve and optimize their training workflows. I'll talk more about that in a bit. Even more impressive was the growth in our non-AI customers. Our AI-native customers cohort continue to grow and diversify rapidly, both in the number of customers we serve and the scale of those customers. our ai-native customers cohort continue to grow and diversify rapidly both in the number of customers we serve and the scale of those customers This quarter included new land deals with two of the world's biggest AI research teams, helping them improve and optimize their training workflows. this quarter included new land deals with two of the world's biggest ai research teams helping them improve and optimize their training workflows I'll talk more about that in a bit. i'll talk more about that in a bit Even more impressive was the growth in our non-AI customers. even more impressive was the growth in our non-ai customers Non-AI customer revenue growth accelerated again this quarter to mid-20s% year-over-year, up from 23% last quarter and 19% in the year-ago quarter. We think this is a sign of strong continued cloud migration, greater adoption of our products, and customers of all kinds accelerating their use of AI. Non-AI customer revenue growth accelerated again this quarter to mid-20s% year-over-year, up from 23% last quarter and 19% in the year-ago quarter. non-ai customer revenue growth accelerated again this quarter to mid-20s% year-over-year up from 23% last quarter and 19% in the year-ago quarter We think this is a sign of strong continued cloud migration, greater adoption of our products, and customers of all kinds accelerating their use of AI. we think this is a sign of strong continued cloud migration greater adoption of our products and customers of all kinds accelerating their use of ai Finally, churn has remained low, with gross revenue retention stable in the mid-to-high 90s, highlighting the mission-critical nature of our platform for our customers. Regarding our Q1 financial performance and key metrics, revenue was $1.1 billion, an increase of 32% year-over-year and above the high end of our guidance range. We ended Q1 with about 33,200 customers, up from about 30,500 a year ago. Finally, churn has remained low, with gross revenue retention stable in the mid-to-high 90s, highlighting the mission-critical nature of our platform for our customers. finally churn has remained low with gross revenue retention stable in the mid-to-high 90s highlighting the mission-critical nature of our platform for our customers Regarding our Q1 financial performance and key metrics, revenue was $1.1 billion, an increase of 32% year-over-year and above the high end of our guidance range. regarding our q1 financial performance and key metrics revenue was $1.1 billion an increase of 32% year-over-year and above the high end of our guidance range We ended Q1 with about 33,200 customers, up from about 30,500 a year ago. we ended q1 with about 33,200 customers up from about 30,500 a year ago We also ended with about 4,550 customers with an ARR of $100,000 or more, up from about 3,770 a year ago. These customers generated about 90% of our ARR. We generated free cash flow of $289 million, with a free cash flow margin of 29%. Turning to product adoption, our platform strategy continues to resonate in the market. For example, 56% of our customers now use four or more products, up from 51% a year ago. We also ended with about 4,550 customers with an ARR of $100,000 or more, up from about 3,770 a year ago. we also ended with about 4,550 customers with an arr of $100,000 or more up from about 3,770 a year ago These customers generated about 90% of our ARR. these customers generated about 90% of our arr We generated free cash flow of $289 million, with a free cash flow margin of 29%. we generated free cash flow of $289 million with a free cash flow margin of 29% Turning to product adoption, our platform strategy continues to resonate in the market. turning to product adoption our platform strategy continues to resonate in the market For example, 56% of our customers now use four or more products, up from 51% a year ago. for example 56% of our customers now use four or more products up from 51% a year ago 35% of our customers use six or more products, up from 28% a year ago, and 20% of our customers use eight or more products, up from 13% a year ago. We're landing more customers and delivering value across more products, and our business continues to grow. Our total ARR now exceeds $4 billion, and our quarterly revenue exceeded $1 billion for the first time in Q1. This is a big achievement for all of us at Datadog and is a product of years of investment in building and innovating for our customers. 35% of our customers use six or more products, up from 28% a year ago, and 20% of our customers use eight or more products, up from 13% a year ago. 35% of our customers use six or more products up from 28% a year ago and 20% of our customers use eight or more products up from 13% a year ago We're landing more customers and delivering value across more products, and our business continues to grow. we're landing more customers and delivering value across more products and our business continues to grow Our total ARR now exceeds $4 billion, and our quarterly revenue exceeded $1 billion for the first time in Q1. our total arr now exceeds $4 billion and our quarterly revenue exceeded $1 billion for the first time in q1 This is a big achievement for all of us at Datadog and is a product of years of investment in building and innovating for our customers. this is a big achievement for all of us at datadog and is a product of years of investment in building and innovating for our customers We're still just getting started. Of our 26 products, five are over $100 million in ARR, and another three are between $50 million-$100 million in ARR. We're working hard to build and deliver further growth in those products. This leaves 18 other products which are earlier in their life cycles. We believe each has the potential to grow to more than $100 million over time. Moving on to R&D. Our engineers, enabled with the latest AI coding tools, are building rapidly to help our customers confidently and securely deploy their applications. We're still just getting started. we're still just getting started Of our 26 products, five are over $100 million in ARR, and another three are between $50 million-$100 million in ARR. of our 26 products five are over $100 million in arr and another three are between $50 million-$100 million in arr We're working hard to build and deliver further growth in those products. we're working hard to build and deliver further growth in those products This leaves 18 other products which are earlier in their life cycles. this leaves 18 other products which are earlier in their life cycles We believe each has the potential to grow to more than $100 million over time. we believe each has the potential to grow to more than $100 million over time Moving on to R&D. moving on to r&d Our engineers, enabled with the latest AI coding tools, are building rapidly to help our customers confidently and securely deploy their applications. our engineers enabled with the latest ai coding tools are building rapidly to help our customers confidently and securely deploy their applications Let me speak to a few of our product launches this quarter. Let's start with AI. As a reminder, we're talking about our AI efforts in two buckets, AI for Datadog and Datadog for AI. First, AI for Datadog. These are AI products and capabilities that make the Datadog platform better and more useful for our customers. In March, we launched our MCP Server for general availability. With MCP Server, developers access live production data to debug their applications directly in their AI coding agent or IDE. Let me speak to a few of our product launches this quarter. let me speak to a few of our product launches this quarter Let's start with AI. let's start with ai As a reminder, we're talking about our AI efforts in two buckets, AI for Datadog and Datadog for AI. as a reminder we're talking about our ai efforts in two buckets ai for datadog and datadog for ai First, AI for Datadog. first ai for datadog These are AI products and capabilities that make the Datadog platform better and more useful for our customers. these are ai products and capabilities that make the datadog platform better and more useful for our customers In March, we launched our MCP Server for general availability. With MCP Server, developers access live production data to debug their applications directly in their AI coding agent or IDE. in march we launched our mcp server for general availability. with mcp server developers access live production data to debug their applications directly in their ai coding agent or ide We delivered Bits AI Security Analyst, which autonomously triages Datadog Cloud SIEM signals, conducts in-depth investigations of potential threats, and delivers actionable recommendations. We've seen Bits AI Security Analyst reduce investigations that could take hours to as little as 30 seconds. We also shipped Bits Assistant, now in preview, which helps customers search and act across Datadog using natural language prompts. We delivered Bits AI Security Analyst, which autonomously triages Datadog Cloud SIEM signals, conducts in-depth investigations of potential threats, and delivers actionable recommendations. we delivered bits ai security analyst which autonomously triages datadog cloud siem signals conducts in-depth investigations of potential threats and delivers actionable recommendations We've seen Bits AI Security Analyst reduce investigations that could take hours to as little as 30 seconds. we've seen bits ai security analyst reduce investigations that could take hours to as little as 30 seconds We also shipped Bits Assistant, now in preview, which helps customers search and act across Datadog using natural language prompts. we also shipped bits assistant now in preview which helps customers search and act across datadog using natural language prompts Moving on to Datadog for AI. This includes Datadog capabilities that deliver end-to-end observability and security across the AI stack. We launched GPU Monitoring, enabling teams to understand GPU fleet utilization, workload efficiency, thermal and power behavior, and interconnect performance. This drives higher GPU ROI and operational reliability. Our customers continue to move forward with their AI activities, and we can see that in their usage of the Datadog platform. Moving on to Datadog for AI. moving on to datadog for ai This includes Datadog capabilities that deliver end-to-end observability and security across the AI stack. this includes datadog capabilities that deliver end-to-end observability and security across the ai stack We launched GPU Monitoring, enabling teams to understand GPU fleet utilization, workload efficiency, thermal and power behavior, and interconnect performance. we launched gpu monitoring enabling teams to understand gpu fleet utilization workload efficiency thermal and power behavior and interconnect performance This drives higher GPU ROI and operational reliability. this drives higher gpu roi and operational reliability Our customers continue to move forward with their AI activities, and we can see that in their usage of the Datadog platform. our customers continue to move forward with their ai activities and we can see that in their usage of the datadog platform We now have over 6,500 customers sending data for one or more of our AI integrations. Though this is only 20% of total customers, they represent about 80% of our ARR. Our customers' usage of AI within Datadog platform continues to grow rapidly. Bits AI SRE agent investigations have more than doubled from December to March. The number of spans sent to our LLM Observability product nearly tripled quarter-over-quarter. We now have over 6,500 customers sending data for one or more of our AI integrations. we now have over 6,500 customers sending data for one or more of our ai integrations Though this is only 20% of total customers, they represent about 80% of our ARR. though this is only 20% of total customers they represent about 80% of our arr Our customers' usage of AI within Datadog platform continues to grow rapidly. our customers' usage of ai within datadog platform continues to grow rapidly Bits AI SRE agent investigations have more than doubled from December to March. bits ai sre agent investigations have more than doubled from december to march The number of spans sent to our LLM Observability product nearly tripled quarter-over-quarter. the number of spans sent to our llm observability product nearly tripled quarter-over-quarter The number of Datadog MCP Server tool calls quadrupled quarter-over-quarter, and the number of Bits Assistant messages increased by a factor of 12 in that period. While we are aggressively building with and for AI, we also continue to expand the Datadog platform to deliver against our customers' increasingly complex needs. To speak to a few of these efforts, last month, we launched Experiments for general availability. The number of Datadog MCP Server tool calls quadrupled quarter-over-quarter, and the number of Bits Assistant messages increased by a factor of 12 in that period. the number of datadog mcp server tool calls quadrupled quarter-over-quarter and the number of bits assistant messages increased by a factor of 12 in that period While we are aggressively building with and for AI, we also continue to expand the Datadog platform to deliver against our customers' increasingly complex needs. while we are aggressively building with and for ai we also continue to expand the datadog platform to deliver against our customers' increasingly complex needs To speak to a few of these efforts, last month, we launched Experiments for general availability. to speak to a few of these efforts last month we launched experiments for general availability Experiments work hand in hand with our feature flagging product and combine best-in-class statistical methods with real-time observability guardrails so companies can test for impact, choose among alternatives quickly, and ship with confidence. In addition, our customers now benefit from APM Recommendations. By analyzing telemetry data from application performance monitoring, user monitoring, profiler, and database monitoring, APM Recommendations automatically identify performance and reliability issues, and most importantly, explain how to fix them. Experiments work hand in hand with our feature flagging product and combine best-in-class statistical methods with real-time observability guardrails so companies can test for impact, choose among alternatives quickly, and ship with confidence. experiments work hand in hand with our feature flagging product and combine best-in-class statistical methods with real-time observability guardrails so companies can test for impact choose among alternatives quickly and ship with confidence In addition, our customers now benefit from APM Recommendations. in addition our customers now benefit from apm recommendations By analyzing telemetry data from application performance monitoring, user monitoring, profiler, and database monitoring, APM Recommendations automatically identify performance and reliability issues, and most importantly, explain how to fix them. by analyzing telemetry data from application performance monitoring user monitoring profiler and database monitoring apm recommendations automatically identify performance and reliability issues and most importantly explain how to fix them We announced our plans to launch our next data center in the U.K. We see a large opportunity to serve our British customers as cloud adoption accelerates in regulated industries. Last but not least, we are pleased to have received FedRAMP High certification from the U.S. federal government. With this certification, we can now move forward with federal agency customers that require FedRAMP High to handle sensitive workloads. We announced our plans to launch our next data center in the U.K. we announced our plans to launch our next data center in the u.k We see a large opportunity to serve our British customers as cloud adoption accelerates in regulated industries. we see a large opportunity to serve our british customers as cloud adoption accelerates in regulated industries Last but not least, we are pleased to have received FedRAMP High certification from the U.S. federal government. last but not least we are pleased to have received fedramp high certification from the u.s federal government With this certification, we can now move forward with federal agency customers that require FedRAMP High to handle sensitive workloads. with this certification we can now move forward with federal agency customers that require fedramp high to handle sensitive workloads Meanwhile, we continue to expand our product offerings, go-to-market teams, and channel partnerships for public sector customers, both in the U.S. and internationally. Our teams were hard at work again, and we're looking forward to sharing many new products and feature announcements at our DASH user conference on June 9th and 10th in New York City. Let's move on to sales and marketing and highlight some of the deals we closed this quarter. Meanwhile, we continue to expand our product offerings, go-to-market teams, and channel partnerships for public sector customers, both in the U.S. and internationally. meanwhile we continue to expand our product offerings go-to-market teams and channel partnerships for public sector customers both in the u.s and internationally Our teams were hard at work again, and we're looking forward to sharing many new products and feature announcements at our DASH user conference on June 9th and 10th in New York City. our teams were hard at work again and we're looking forward to sharing many new products and feature announcements at our dash user conference on june 9th and 10th in new york city Let's move on to sales and marketing and highlight some of the deals we closed this quarter. let's move on to sales and marketing and highlight some of the deals we closed this quarter First, we landed two large deals, a seven-figure and an eight-figure annualized deals with the AI research divisions at two of the world's largest technology companies. These organizations are building and training the most advanced AI models in the world. It is critical for them to reduce engineering friction and increase training velocity. Fragmented internal and open source tooling made it harder to identify and solve issues and reduce engineering and research productivity. First, we landed two large deals, a seven-figure and an eight-figure annualized deals with the AI research divisions at two of the world's largest technology companies. first we landed two large deals a seven-figure and an eight-figure annualized deals with the ai research divisions at two of the world's largest technology companies These organizations are building and training the most advanced AI models in the world. these organizations are building and training the most advanced ai models in the world It is critical for them to reduce engineering friction and increase training velocity. it is critical for them to reduce engineering friction and increase training velocity Fragmented internal and open source tooling made it harder to identify and solve issues and reduce engineering and research productivity. fragmented internal and open source tooling made it harder to identify and solve issues and reduce engineering and research productivity By using Datadog, both companies are accelerating their pace of innovation on their hyperscale AI training workloads. This includes optimizing their workflows using GPU Monitoring on large parallel GPU grids. Next, we signed a seven-figure annualized expansion for an eight-figure annualized deal with a leading online recruiting platform. This customer is centralizing on Datadog to reduce complexity, drive developer velocity, and improve efficiency. By using Datadog, both companies are accelerating their pace of innovation on their hyperscale AI training workloads. by using datadog both companies are accelerating their pace of innovation on their hyperscale ai training workloads This includes optimizing their workflows using GPU Monitoring on large parallel GPU grids. this includes optimizing their workflows using gpu monitoring on large parallel gpu grids Next, we signed a seven-figure annualized expansion for an eight-figure annualized deal with a leading online recruiting platform. next we signed a seven-figure annualized expansion for an eight-figure annualized deal with a leading online recruiting platform This customer is centralizing on Datadog to reduce complexity, drive developer velocity, and improve efficiency. this customer is centralizing on datadog to reduce complexity drive developer velocity and improve efficiency With this expansion, they will replace a standalone tool with Datadog LLM Observability to correlate LLM signals with APM and user experience data. This customer will grow to 16 Datadog products, including Datadog MCP Server. We signed a seven-figure annualized expansion for an eight-figure annualized deal with a Fortune 500 bank. With this expansion, this customer will migrate their remaining log data into Datadog, fully replacing their legacy log vendor. With this expansion, they will replace a standalone tool with Datadog LLM Observability to correlate LLM signals with APM and user experience data. with this expansion they will replace a standalone tool with datadog llm observability to correlate llm signals with apm and user experience data This customer will grow to 16 Datadog products, including Datadog MCP Server. this customer will grow to 16 datadog products including datadog mcp server We signed a seven-figure annualized expansion for an eight-figure annualized deal with a Fortune 500 bank. we signed a seven-figure annualized expansion for an eight-figure annualized deal with a fortune 500 bank With this expansion, this customer will migrate their remaining log data into Datadog, fully replacing their legacy log vendor. with this expansion this customer will migrate their remaining log data into datadog fully replacing their legacy log vendor Most notably, our Flex Logs give them granular control over costs while meeting strict compliance requirements. This customer uses 10 Datadog products, including Bits AI SRE Agent, to accelerate incident response with AI. We signed a seven-figure annualized expansion with a leading global hedge fund. This customer operates thousands of on-prem hosts and network devices. At that scale, their open source monitoring stack has become operationally unsustainable, impacting portfolio managers and investment analysts. Most notably, our Flex Logs give them granular control over costs while meeting strict compliance requirements. most notably our flex logs give them granular control over costs while meeting strict compliance requirements This customer uses 10 Datadog products, including Bits AI SRE Agent, to accelerate incident response with AI. this customer uses 10 datadog products including bits ai sre agent to accelerate incident response with ai We signed a seven-figure annualized expansion with a leading global hedge fund. we signed a seven-figure annualized expansion with a leading global hedge fund This customer operates thousands of on-prem hosts and network devices. this customer operates thousands of on-prem hosts and network devices At that scale, their open source monitoring stack has become operationally unsustainable, impacting portfolio managers and investment analysts. at that scale their open source monitoring stack has become operationally unsustainable impacting portfolio managers and investment analysts With this expansion, they will replace their entire on-prem observability layer with Datadog Infrastructure Monitoring and Network Device Monitoring. It will have unified visibility across their cloud and on-prem environments. This customer will expand to 11 Datadog products. We landed a six-figure annualized deal with a Fortune 500 insurance company. This company's fragmented observability stack led to long outages with incident supported first by their customers instead of their tooling. With this expansion, they will replace their entire on-prem observability layer with Datadog Infrastructure Monitoring and Network Device Monitoring. with this expansion they will replace their entire on-prem observability layer with datadog infrastructure monitoring and network device monitoring It will have unified visibility across their cloud and on-prem environments. it will have unified visibility across their cloud and on-prem environments This customer will expand to 11 Datadog products. this customer will expand to 11 datadog products We landed a six-figure annualized deal with a Fortune 500 insurance company. This company's fragmented observability stack led to long outages with incident supported first by their customers instead of their tooling. we landed a six-figure annualized deal with a fortune 500 insurance company. this company's fragmented observability stack led to long outages with incident supported first by their customers instead of their tooling By using Datadog and consolidating three legacy APM tools, they expect to move from reactive responses to proactive incident detection. They will adopt 10 Datadog products to start, including all three pillars and LLM Observability. Next, we signed a seven-figure annualized expansion with one of the world's largest travel groups in APAC. This customer was using Datadog on one business unit, but in two others, they were juggling multiple tools and lacked actionable insights. By using Datadog and consolidating three legacy APM tools, they expect to move from reactive responses to proactive incident detection. by using datadog and consolidating three legacy apm tools they expect to move from reactive responses to proactive incident detection They will adopt 10 Datadog products to start, including all three pillars and LLM Observability. they will adopt 10 datadog products to start including all three pillars and llm observability Next, we signed a seven-figure annualized expansion with one of the world's largest travel groups in APAC. next, we signed a seven-figure annualized expansion with one of the world's largest travel groups in apac This customer was using Datadog on one business unit, but in two others, they were juggling multiple tools and lacked actionable insights. this customer was using datadog on one business unit but in two others they were juggling multiple tools and lacked actionable insights By consolidating six legacy open source and cloud monitoring tools, the customers save money and improve platform resiliency and performance. This multi-year commitment positions Datadog as their strategic observability provider. Finally, we landed a six-figure annualized deal with a leading Latin American fintech company. This customer serves tens of millions of users across critical financial flows. By consolidating six legacy open source and cloud monitoring tools, the customers save money and improve platform resiliency and performance. by consolidating six legacy open source and cloud monitoring tools the customers save money and improve platform resiliency and performance This multi-year commitment positions Datadog as their strategic observability provider. this multi-year commitment positions datadog as their strategic observability provider Finally, we landed a six-figure annualized deal with a leading Latin American fintech company. finally we landed a six-figure annualized deal with a leading latin american fintech company This customer serves tens of millions of users across critical financial flows. this customer serves tens of millions of users across critical financial flows Their rapid growth outpaced their fragmented front-end monitoring setup, and outages exposed them to financial, operational, and reputational risks. By adopting our Digital Experience Monitoring suite, including RUM, Synthetics, and Product Analytics, they now have full visibility over user activities with the cost control they also previously lacked. This customer will start with 5 Datadog products. That's it for our wins. Congratulations again to our entire go-to-market organization for a great Q1. Their rapid growth outpaced their fragmented front-end monitoring setup, and outages exposed them to financial, operational, and reputational risks. their rapid growth outpaced their fragmented front-end monitoring setup and outages exposed them to financial operational and reputational risks By adopting our Digital Experience Monitoring suite, including RUM, Synthetics, and Product Analytics, they now have full visibility over user activities with the cost control they also previously lacked. by adopting our digital experience monitoring suite including rum synthetics and product analytics they now have full visibility over user activities with the cost control they also previously lacked This customer will start with 5 Datadog products. this customer will start with 5 datadog products That's it for our wins. that's it for our wins Congratulations again to our entire go-to-market organization for a great Q1. congratulations again to our entire go-to-market organization for a great q1 Before I turn it over to David for a financial review, I want to say a few words on our longer term outlook. We are pleased with the way we started 2026 as we support our customers' inflection in AI usage and application development, and as they lean into our AI innovations, including Bits AI SRE, Bits AI Security Analyst, Bits Assistant, Datadog MCP Server, GPU Monitoring, and many more. Before I turn it over to David for a financial review, I want to say a few words on our longer term outlook. before i turn it over to david for a financial review i want to say a few words on our longer term outlook We are pleased with the way we started 2026 as we support our customers' inflection in AI usage and application development, and as they lean into our AI innovations, including Bits AI SRE, Bits AI Security Analyst, Bits Assistant, Datadog MCP Server, GPU Monitoring, and many more. we are pleased with the way we started 2026 as we support our customers' inflection in ai usage and application development and as they lean into our ai innovations including bits ai sre bits ai security analyst bits assistant datadog mcp server gpu monitoring and many more There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business. We now have an additional secular growth driver with AI as we help our customers deliver more value with this transformative new technology. Now more than ever, we feel ideally positioned to help customers of every size and every industry, as well as all type of users, whether humans or AI agents, so they can transform, innovate, and drive value through AI and cloud adoption. With that, I will turn it over to our CFO, David. There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business. there is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers for our business We now have an additional secular growth driver with AI as we help our customers deliver more value with this transformative new technology. we now have an additional secular growth driver with ai as we help our customers deliver more value with this transformative new technology Now more than ever, we feel ideally positioned to help customers of every size and every industry, as well as all type of users, whether humans or AI agents, so they can transform, innovate, and drive value through AI and cloud adoption. now more than ever we feel ideally positioned to help customers of every size and every industry as well as all type of users whether humans or ai agents so they can transform innovate and drive value through ai and cloud adoption With that, I will turn it over to our CFO, David. with that i will turn it over to our cfo david

Speaker 1: Thanks, Olivier. This was a very strong quarter for Datadog. Our Q1 revenue was $1.01 billion, up 32% year-over-year. Our 6% quarter-over-quarter revenue growth is the highest for a Q1 since 2022, and our $53 million quarter-over-quarter revenue added is the highest ever for a Q1. That included the strongest quarter of sequential usage growth from existing customers since the first quarter of 2022. Thanks, Olivier. thanks olivier This was a very strong quarter for Datadog. this was a very strong quarter for datadog Our Q1 revenue was $1.01 billion, up 32% year-over-year. our q1 revenue was $1.01 billion up 32% year-over-year Our 6% quarter-over-quarter revenue growth is the highest for a Q1 since 2022, and our $53 million quarter-over-quarter revenue added is the highest ever for a Q1. our 6% quarter-over-quarter revenue growth is the highest for a q1 since 2022 and our $53 million quarter-over-quarter revenue added is the highest ever for a q1 That included the strongest quarter of sequential usage growth from existing customers since the first quarter of 2022. that included the strongest quarter of sequential usage growth from existing customers since the first quarter of 2022 We also delivered an all-time record for sequential ARR added to the quarter. ARR growth accelerated in each month of Q1, and we see a continuation of these healthy growth trends in April. We also achieved strong new logo bookings. New logo annualized bookings set a new all-time record by a significant margin and more than double versus a year ago quarter. We also delivered an all-time record for sequential ARR added to the quarter. we also delivered an all-time record for sequential arr added to the quarter ARR growth accelerated in each month of Q1, and we see a continuation of these healthy growth trends in April. arr growth accelerated in each month of q1 and we see a continuation of these healthy growth trends in april We also achieved strong new logo bookings. we also achieved strong new logo bookings New logo annualized bookings set a new all-time record by a significant margin and more than double versus a year ago quarter. new logo annualized bookings set a new all-time record by a significant margin and more than double versus a year ago quarter These included wins in observability and included some of our newer products like security, Data Observability, and Flex Logs. Our new logo average land size also set a record and more than doubled year-over-year as we continue to land larger deals. Revenue growth accelerated with our broad base of customers, excluding the AI natives, to mid-20s percent year-over-year, up from 23% last quarter and 19% in the year-ago quarter. These included wins in observability and included some of our newer products like security, Data Observability, and Flex Logs. these included wins in observability and included some of our newer products like security data observability and flex logs Our new logo average land size also set a record and more than doubled year-over-year as we continue to land larger deals. our new logo average land size also set a record and more than doubled year-over-year as we continue to land larger deals Revenue growth accelerated with our broad base of customers, excluding the AI natives, to mid-20s percent year-over-year, up from 23% last quarter and 19% in the year-ago quarter. revenue growth accelerated with our broad base of customers excluding the ai natives to mid-20s percent year-over-year up from 23% last quarter and 19% in the year-ago quarter We saw robust growth across our customer base with broad-based strength across customer size, spending bands, and industries. Meanwhile, our AI native customer growth continues to significantly outpace the rest of the business. This group continues to diversify and grow, including 22 customers spending more than $1 million annually and five spending more than $10 million annually. We saw robust growth across our customer base with broad-based strength across customer size, spending bands, and industries. we saw robust growth across our customer base with broad-based strength across customer size spending bands and industries Meanwhile, our AI native customer growth continues to significantly outpace the rest of the business. meanwhile our ai native customer growth continues to significantly outpace the rest of the business This group continues to diversify and grow, including 22 customers spending more than $1 million annually and five spending more than $10 million annually. this group continues to diversify and grow including 22 customers spending more than $1 million annually and five spending more than $10 million annually This group includes the leading companies in foundational models, code-gen tools, and vertical specific AI solutions. Next, regarding our retention metrics. Our trailing-12-month net revenue retention percentage was in the low 120%, up from about 120 last quarter, and our trailing-12-month gross retention percentage remains in the mid to high 90s. Now moving on to our financial results. This group includes the leading companies in foundational models, code-gen tools, and vertical specific AI solutions. this group includes the leading companies in foundational models code-gen tools and vertical specific ai solutions Next, regarding our retention metrics. next regarding our retention metrics Our trailing-12-month net revenue retention percentage was in the low 120%, up from about 120 last quarter, and our trailing-12-month gross retention percentage remains in the mid to high 90s. our trailing-12-month net revenue retention percentage was in the low 120% up from about 120 last quarter and our trailing-12-month gross retention percentage remains in the mid to high 90s Now moving on to our financial results. now moving on to our financial results Billings were $1.03 billion, up 37% year-over-year, and remaining performance obligations, or RPO, was $3.48 billion, up 51% year-over-year, with current RPO growing in the mid-40s percent year-over-year. RPO duration increased year-over-year as the mix of multi-year deals increased in Q1. As a reminder, we continue to believe revenue is a better indicator of our business trends than billings and RPO, given their variability. Billings were $1.03 billion, up 37% year-over-year, and remaining performance obligations, or RPO, was $3.48 billion, up 51% year-over-year, with current RPO growing in the mid-40s percent year-over-year. billings were $1.03 billion up 37% year-over-year and remaining performance obligations or rpo was $3.48 billion up 51% year-over-year with current rpo growing in the mid-40s percent year-over-year RPO duration increased year-over-year as the mix of multi-year deals increased in Q1. As a reminder, we continue to believe revenue is a better indicator of our business trends than billings and RPO, given their variability. rpo duration increased year-over-year as the mix of multi-year deals increased in q1. as a reminder we continue to believe revenue is a better indicator of our business trends than billings and rpo given their variability Now let's review some of the key income statement results. Unless otherwise noted, all metrics are non-GAAP, and we have provided a reconciliation of GAAP to non-GAAP financials in our earnings release. First, Q1 gross profit was $807 million with a gross margin of 80.2%. Now let's review some of the key income statement results. now let's review some of the key income statement results Unless otherwise noted, all metrics are non-GAAP, and we have provided a reconciliation of GAAP to non-GAAP financials in our earnings release. unless otherwise noted all metrics are non-gaap and we have provided a reconciliation of gaap to non-gaap financials in our earnings release First, Q1 gross profit was $807 million with a gross margin of 80.2%. first q1 gross profit was $807 million with a gross margin of 80.2% This compares to a gross margin of 81.4% last quarter and 80.3% in the year-ago quarter. As we've discussed in the past, our gross margin varies from quarter-to-quarter, with investments into innovations for our customers offset by efficiency efforts. Our Q1 OpEx grew 31% year-over-year versus 29% last quarter and 29% in the year-ago quarter. This compares to a gross margin of 81.4% last quarter and 80.3% in the year-ago quarter. this compares to a gross margin of 81.4% last quarter and 80.3% in the year-ago quarter As we've discussed in the past, our gross margin varies from quarter- to- quarter, with investments into innovations for our customers offset by efficiency efforts. as we've discussed in the past our gross margin varies from quarter- to- quarter with investments into innovations for our customers offset by efficiency efforts Our Q1 OpEx grew 31% year-over-year versus 29% last quarter and 29% in the year-ago quarter. our q1 opex grew 31% year-over-year versus 29% last quarter and 29% in the year-ago quarter As a reminder, we continue to grow our investments to pursue our long-term growth opportunities, and this OpEx growth is an indication of our execution of our hiring plans. Q1 operating income was $223 million for a 22% operating margin, compared to 24% last quarter and 22% in the year ago quarter. As a reminder, we continue to grow our investments to pursue our long-term growth opportunities, and this OpEx growth is an indication of our execution of our hiring plans. as a reminder we continue to grow our investments to pursue our long-term growth opportunities and this opex growth is an indication of our execution of our hiring plans Q1 operating income was $223 million for a 22% operating margin, compared to 24% last quarter and 22% in the year ago quarter. q1 operating income was $223 million for a 22% operating margin compared to 24% last quarter and 22% in the year ago quarter Turning to the balance sheet and cash flow statements. We ended the quarter with $4.8 billion in cash equivalents and marketable securities. Our cash flow from operations was $335 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $289 million, and free cash flow margin was 29%. Now for our outlook for the second quarter and for the fiscal year 2026. Turning to the balance sheet and cash flow statements. turning to the balance sheet and cash flow statements We ended the quarter with $4.8 billion in cash equivalents and marketable securities. we ended the quarter with $4.8 billion in cash equivalents and marketable securities Our cash flow from operations was $335 million in the quarter. our cash flow from operations was $335 million in the quarter After taking into consideration capital expenditures and capitalized software, free cash flow was $289 million, and free cash flow margin was 29%. after taking into consideration capital expenditures and capitalized software free cash flow was $289 million and free cash flow margin was 29% Now for our outlook for the second quarter and for the fiscal year 2026. now for our outlook for the second quarter and for the fiscal year 2026 First, our guidance philosophy overall remains unchanged. As a reminder, we base our guidance on trends observed in recent months and apply conservativism on these growth trends. In addition, as with last quarter, we are applying a higher degree of conservativism to our largest customer. First, our guidance philosophy overall remains unchanged. first our guidance philosophy overall remains unchanged As a reminder, we base our guidance on trends observed in recent months and apply conservativism on these growth trends. as a reminder we base our guidance on trends observed in recent months and apply conservativism on these growth trends In addition, as with last quarter, we are applying a higher degree of conservativism to our largest customer. in addition as with last quarter we are applying a higher degree of conservativism to our largest customer For the second quarter, we expect revenues to be in the range of $1.07 billion-$1.08 billion, which represents a 29%-31% year-over-year growth. This guidance implies sequential revenue growth of $64 million-$74 million, or 6%-7%, due to the strong growth of revenue in Q1 and into April. Non-GAAP operating income is expected to be in the range of $225 million-$235 million, which implies an operating margin of 21%-22%. For the second quarter, we expect revenues to be in the range of $1.07 billion-$1.08 billion, which represents a 29%-31% year-over-year growth. for the second quarter we expect revenues to be in the range of $1.07 billion-$1.08 billion which represents a 29%-31% year-over-year growth This guidance implies sequential revenue growth of $64 million-$74 million, or 6%-7%, due to the strong growth of revenue in Q1 and into April. this guidance implies sequential revenue growth of $64 million-$74 million or 6%-7% due to the strong growth of revenue in q1 and into april Non-GAAP operating income is expected to be in the range of $225 million-$235 million, which implies an operating margin of 21%-22%. non-gaap operating income is expected to be in the range of $225 million-$235 million which implies an operating margin of 21%-22% As a reminder, in Q2 we will be holding our Dash User Conference, which we estimate to cost about $15 million and which we have reflected in our operating income guidance. Non-GAAP net income per share is expected to be $0.57-$0.59 per share based on approximately 369 million weighted average diluted shares outstanding. For fiscal 2026, we expect revenues to be in the range of $4.3 billion-$4.34 billion, which represents 25%-27% year-over-year growth. Non-GAAP operating income is expected to be in the range of $940 million-$980 million, which implies an operating margin of 22%-23%. As a reminder, in Q2 we will be holding our Dash User Conference, which we estimate to cost about $15 million and which we have reflected in our operating income guidance. as a reminder in q2 we will be holding our dash user conference which we estimate to cost about $15 million and which we have reflected in our operating income guidance Non-GAAP net income per share is expected to be $0.57-$0.59 per share based on approximately 369 million weighted average diluted shares outstanding. non-gaap net income per share is expected to be $0.57-$0.59 per share based on approximately 369 million weighted average diluted shares outstanding For fiscal 2026, we expect revenues to be in the range of $4.3 billion-$4.34 billion, which represents 25%-27% year-over-year growth. for fiscal 2026 we expect revenues to be in the range of $4.3 billion-$4.34 billion which represents 25%-27% year-over-year growth Non-GAAP operating income is expected to be in the range of $940 million-$980 million, which implies an operating margin of 22%-23%. non-gaap operating income is expected to be in the range of $940 million-$980 million which implies an operating margin of 22%-23% Non-GAAP net income per share is expected to be in the range of $2.36-$2.44 per share based on approximately 372 million weighted average diluted shares outstanding. Finally, some additional notes on the guidance. We expect net interest and other income for fiscal 2026 to be approximately $170 million. We expect cash taxes for 2026 to be approximately $30 million-$40 million. We continue to apply a 21% non-GAAP tax rate for 2026 and going forward. We expect capital expenditures and capitalized software together to be 4%-5% of revenue in fiscal 2026. Non-GAAP net income per share is expected to be in the range of $2.36-$2.44 per share based on approximately 372 million weighted average diluted shares outstanding. non-gaap net income per share is expected to be in the range of $2.36-$2.44 per share based on approximately 372 million weighted average diluted shares outstanding Finally, some additional notes on the guidance. finally some additional notes on the guidance We expect net interest and other income for fiscal 2026 to be approximately $170 million. we expect net interest and other income for fiscal 2026 to be approximately $170 million We expect cash taxes for 2026 to be approximately $30 million-$40 million. we expect cash taxes for 2026 to be approximately $30 million-$40 million We continue to apply a 21% non-GAAP tax rate for 2026 and going forward. we continue to apply a 21% non-gaap tax rate for 2026 and going forward We expect capital expenditures and capitalized software together to be 4%-5% of revenue in fiscal 2026. we expect capital expenditures and capitalized software together to be 4%-5% of revenue in fiscal 2026 To summarize, we are very pleased with our execution in Q1. We are well-positioned to help our existing and prospective customers with their cloud migration, digital transformation, and AI adoption journeys. I want to thank Datadogs worldwide for their efforts. With that, we'll open the call for questions. Operator, let's begin the Q&A. Thanks. To summarize, we are very pleased with our execution in Q1. to summarize we are very pleased with our execution in q1 We are well- positioned to help our existing and prospective customers with their cloud migration, digital transformation, and AI adoption journeys. we are well- positioned to help our existing and prospective customers with their cloud migration digital transformation and ai adoption journeys I want to thank Datadogs worldwide for their efforts. i want to thank datadogs worldwide for their efforts With that, we'll open the call for questions. with that we'll open the call for questions Operator, let's begin the Q&A. operator let's begin the q&a Thanks. thanks

Speaker 3: Thank you. As a reminder, if you would like to ask a question, please press star one one on your telephone. You'll hear the automated message advising your hand is raised. We also ask that you please wait for your name and company to be announced before proceeding with your question. One moment while we compile the Q&A roster. Our first question today is coming from the line of Mark Murphy of JPMorgan. Please go ahead. Thank you. thank you As a reminder, if you would like to ask a question, please press star one one on your telephone. as a reminder if you would like to ask a question please press star one one on your telephone You'll hear the automated message advising your hand is raised. you'll hear the automated message advising your hand is raised We also ask that you please wait for your name and company to be announced before proceeding with your question. we also ask that you please wait for your name and company to be announced before proceeding with your question One moment while we compile the Q&A roster. one moment while we compile the q&a roster Our first question today is coming from the line of Mark Murphy of JP Morgan. our first question today is coming from the line of mark murphy of jp morgan Please go ahead. please go ahead

Speaker 6: Thank you so much, and congratulations on an amazing performance. Olivier, is there any way to conceptualize the growth in the sheer raw volume of code that's being produced in the world today due to adoption of code generators such as Claude Code and Codex and Cursor because they seem to be developing the capability to take on full projects. As some of the charts are showing, these capabilities are just exponentially exploding upward in a straight line. I'm wondering how much of that code is going into production and, therefore driving activity for Datadog. Thank you so much, and congratulations on an amazing performance. thank you so much and congratulations on an amazing performance Olivier, is there any way to conceptualize the growth in the sheer raw volume of code that's being produced in the world today due to adoption of code generators such as Claude Code and Codex and Cursor because they seem to be developing the capability to take on full projects. olivier is there any way to conceptualize the growth in the sheer raw volume of code that's being produced in the world today due to adoption of code generators such as claude code and codex and cursor because they seem to be developing the capability to take on full projects As some of the charts are showing, these capabilities are just exponentially exploding upward in a straight line. as some of the charts are showing these capabilities are just exponentially exploding upward in a straight line I'm wondering how much of that code is going into production and, therefore driving activity for Datadog. i'm wondering how much of that code is going into production and therefore driving activity for datadog

Speaker 2: Well, we definitely think and see that there's many more applications being created. There's gonna be way more complexity in production. We see some of that happening already today. Some of those new applications are getting into production. They are finding users. We see some signs of that at every layer of our platform. Well, we definitely think and see that there's many more applications being created. well we definitely think and see that there's many more applications being created There's gonna be way more complexity in production. there's gonna be way more complexity in production We see some of that happening already today. we see some of that happening already today Some of those new applications are getting into production. some of those new applications are getting into production They are finding users. they are finding users We see some signs of that at every layer of our platform. we see some signs of that at every layer of our platform You know, we quoted a few stats on the increasing data volumes we see in our AI products. That's definitely a reflection of that. We see an inflection point there in consumption from customers. We see a move to production that is very real, and we see that across both AI native and non-AI companies. You know, we quoted a few stats on the increasing data volumes we see in our AI products. you know we quoted a few stats on the increasing data volumes we see in our ai products That's definitely a reflection of that. that's definitely a reflection of that We see an inflection point there in consumption from customers. we see an inflection point there in consumption from customers We see a move to production that is very real, and we see that across both AI native and non-AI companies. we see a move to production that is very real and we see that across both ai native and non-ai companies

Speaker 6: Okay. Thank you. As just a quick related follow-up. If we click down one layer, you know, I'm wondering how you might view the increasing heterogeneity of the environments at the silicon level. Because when you look across Amazon with Trainium and Graviton, Google with TPUs, Microsoft has launched the Maia silicon, it looks like that is starting to explode. Okay. okay Thank you. thank you As just a quick related follow-up. as just a quick related follow-up If we click down one layer, you know, I'm wondering how you might view the increasing heterogeneity of the environments at the silicon level. if we click down one layer you know i'm wondering how you might view the increasing heterogeneity of the environments at the silicon level Because when you look across Amazon with Trainium and Graviton, Google with TPUs, Microsoft has launched the Maia silicon, it looks like that is starting to explode. because when you look across amazon with trainium and graviton google with tpus microsoft has launched the maia silicon it looks like that is starting to explode You know, our understanding is that trying to monitor the mixed environments is a lot more difficult than if you just have a uniform fleet of Intel and AMD chips. We keep hearing all the traditional monitoring tools, because they really fail on the custom silicon and Datadog handles it well. All this new telemetry, including high bandwidth memory and that type of thing, could you speak to whether that trend is giving you some tailwind? You know, our understanding is that trying to monitor the mixed environments is a lot more difficult than if you just have a uniform fleet of Intel and AMD chips. you know our understanding is that trying to monitor the mixed environments is a lot more difficult than if you just have a uniform fleet of intel and amd chips We keep hearing all the traditional monitoring tools, because they really fail on the custom silicon and Datadog handles it well. we keep hearing all the traditional monitoring tools because they really fail on the custom silicon and datadog handles it well All this new telemetry, including high bandwidth memory and that type of thing, could you speak to whether that trend is giving you some tailwind? all this new telemetry including high bandwidth memory and that type of thing could you speak to whether that trend is giving you some tailwind

Speaker 2: Yeah. I mean, look, the broader market that's interesting here is. You know, training used to be something only two or three companies were doing or maybe four or five, at a large scale. It looks like training actually might democratize quite a bit more, and many companies will train models on a regular basis. It becomes more of a viable category for service provider like us, basically. I think the heterogeneity of the silicon is definitely a trend that plays in our favor there. Yeah. yeah I mean, look, the broader market that's interesting here is. i mean look the broader market that's interesting here is You know, training used to be something only two or three companies were doing or maybe four or five, at a large scale. you know training used to be something only two or three companies were doing or maybe four or five at a large scale It looks like training actually might democratize quite a bit more, and many companies will train models on a regular basis. it looks like training actually might democratize quite a bit more and many companies will train models on a regular basis It becomes more of a viable category for service provider like us, basically. it becomes more of a viable category for service provider like us basically I think the heterogeneity of the silicon is definitely a trend that plays in our favor there. i think the heterogeneity of the silicon is definitely a trend that plays in our favor there You know, the more heterogeneous, the more you need someone else to make sense of everything for you and tie it all together and also tie it all with the non-GPU aspects and the rest of the infrastructure and the application and the users and the developers, like, basically everything we do for you. There's only You know, when you think of who actually has heterogeneous environments today, that is still a very small number of companies. You know, Google barely just started selling their TPUs to the outside. You know, I think it's still a small number of companies out that are there, but we see a growing opportunity there. You know, the more heterogeneous, the more you need someone else to make sense of everything for you and tie it all together and also tie it all with the non-GPU aspects and the rest of the infrastructure and the application and the users and the developers, like, basically everything we do for you. you know the more heterogeneous the more you need someone else to make sense of everything for you and tie it all together and also tie it all with the non-gpu aspects and the rest of the infrastructure and the application and the users and the developers like basically everything we do for you There's only You know, when you think of who actually has heterogeneous environments today, that is still a very small number of companies. there's only you know when you think of who actually has heterogeneous environments today that is still a very small number of companies You know, Google barely just started selling their TPUs to the outside. you know google barely just started selling their tpus to the outside You know, I think it's still a small number of companies out that are there, but we see a growing opportunity there. you know i think it's still a small number of companies out that are there but we see a growing opportunity there Interestingly, you know, last year when we reported earnings, we said we're mostly interested in inference workloads and training is not really a market for us yet. Now we actually see training becoming a market. We started landing customers that are actually hyperscalers that have a whole host of homegrown technologies, and that are using us specifically in their super intelligence labs to help monitor their workloads, accelerate the training runs, monitor the GPUs also. We see that as a point of validation that there's a, there's gonna be a good market for us there. Interestingly, you know, last year when we reported earnings, we said we're mostly interested in inference workloads and training is not really a market for us yet. interestingly you know last year when we reported earnings we said we're mostly interested in inference workloads and training is not really a market for us yet Now we actually see training becoming a market. now we actually see training becoming a market We started landing customers that are actually hyperscalers that have a whole host of homegrown technologies, and that are using us specifically in their super intelligence labs to help monitor their workloads, accelerate the training runs, monitor the GPUs also. we started landing customers that are actually hyperscalers that have a whole host of homegrown technologies and that are using us specifically in their super intelligence labs to help monitor their workloads accelerate the training runs monitor the gpus also We see that as a point of validation that there's a, there's gonna be a good market for us there. we see that as a point of validation that there's a there's gonna be a good market for us there

Speaker 6: Well, that's amazing to think there's a whole new dimension where if you can move from inferencing into the training side. I caught the reference in the prepared remarks of how you landed a couple of those very large labs. Congrats on everything. Thank you for taking my questions. Well, that's amazing to think there's a whole new dimension where if you can move from inferencing into the training side. well that's amazing to think there's a whole new dimension where if you can move from inferencing into the training side I caught the reference in the prepared remarks of how you landed a couple of those very large labs. i caught the reference in the prepared remarks of how you landed a couple of those very large labs Congrats on everything. congrats on everything Thank you for taking my questions. thank you for taking my questions

Speaker 3: Thank you. One moment for the next question. Our next question will be coming from the line of Sanjit Singh of Morgan Stanley. Your line is open. Thank you. thank you One moment for the next question. one moment for the next question Our next question will be coming from the line of Sanjit Singh of Morgan Stanley. our next question will be coming from the line of sanjit singh of morgan stanley Your line is open. your line is open

Speaker 7: Yeah. Thank you for taking the question. I want to start it off with David. You know, this guide to start the year is probably the best we've seen in several years, David, and you laid out the underlying assumptions quite well. Just wanted to do a sanity gut check just on the sort of overall macro backdrop. Yeah. yeah Thank you for taking the question. thank you for taking the question I want to start it off with David. i want to start it off with david You know, this guide to start the year is probably the best we've seen in several years, David, and you laid out the underlying assumptions quite well. you know this guide to start the year is probably the best we've seen in several years david and you laid out the underlying assumptions quite well Just wanted to do a sanity gut check just on the sort of overall macro backdrop. just wanted to do a sanity gut check just on the sort of overall macro backdrop We do have some geopolitical tensions and those types of things when we think about your mid-SMBs business and any impact from like in your, you know, e-commerce or retail business where there may be some, you know, consumer discretionary impacts. I just want to get, like, how you're thinking about those parts of the business, and then I had a follow-up for Ollie. We do have some geopolitical tensions and those types of things when we think about your mid-SMBs business and any impact from like in your, you know, e-commerce or retail business where there may be some, you know, consumer discretionary impacts. we do have some geopolitical tensions and those types of things when we think about your mid-smbs business and any impact from like in your you know e-commerce or retail business where there may be some you know consumer discretionary impacts I just want to get, like, how you're thinking about those parts of the business, and then I had a follow-up for Ollie. i just want to get like how you're thinking about those parts of the business and then i had a follow-up for ollie

Speaker 1: Yeah. We had a very strong quarter across the board. We had, you know, multi-industry, multi-geography type of quarter. SMB was very strong. You know that the source of our, our guidance and our raises are at the, at the core, that type of performance. We haven't seen any particular effect in the consumer businesses or e-commerce businesses yet. We basically have a continuation of trends in those businesses, travels and things like that are very similar to, you know, the other industries. Yeah. yeah We had a very strong quarter across the board. we had a very strong quarter across the board We had, you know, multi-industry, multi-geography type of quarter. we had you know multi-industry multi-geography type of quarter SMB was very strong. smb was very strong You know that the source of our, our guidance and our raises are at the, at the core, that type of performance. you know that the source of our our guidance and our raises are at the at the core that type of performance We haven't seen any particular effect in the consumer businesses or e-commerce businesses yet. we haven't seen any particular effect in the consumer businesses or e-commerce businesses yet We basically have a continuation of trends in those businesses, travels and things like that are very similar to, you know, the other industries. we basically have a continuation of trends in those businesses travels and things like that are very similar to you know the other industries We haven't seen it yet. We obviously watch it and look at analytics, but we haven't seen it. In terms of our overall guidance. You know, the trends that we have in organic, we discount across the board, and I think we mentioned our particular treatment of our largest customer. We haven't seen it yet. we haven't seen it yet We obviously watch it and look at analytics, but we haven't seen it. we obviously watch it and look at analytics but we haven't seen it In terms of our overall guidance. in terms of our overall guidance You know, the trends that we have in organic, we discount across the board, and I think we mentioned our particular treatment of our largest customer. you know the trends that we have in organic we discount across the board and i think we mentioned our particular treatment of our largest customer

Speaker 7: No, that's very clear. Olivier, for you, I think when we talk to investors about the debate in this category longer term, it's just what does this, what does the category look like when agents are doing the triaging, investigating versus human engineers and human SREs? What is your sort of vision of that, how that evolves for Datadog, both from a product standpoint and an experience standpoint, from a UI perspective. Also, like, is there going to be new modalities in terms of pricing when agents are consuming the Datadog platform to a higher degree than engineers do today? No, that's very clear. no that's very clear Olivier, for you, I think when we talk to investors about the debate in this category longer term, it's just what does this, what does the category look like when agents are doing the triaging, investigating versus human engineers and human SREs? olivier for you i think when we talk to investors about the debate in this category longer term it's just what does this what does the category look like when agents are doing the triaging investigating versus human engineers and human sres What is your sort of vision of that, how that evolves for Datadog, both from a product standpoint and an experience standpoint, from a UI perspective. what is your sort of vision of that how that evolves for datadog both from a product standpoint and an experience standpoint from a ui perspective Also, like, is there going to be new modalities in terms of pricing when agents are consuming the Datadog platform to a higher degree than engineers do today? also like is there going to be new modalities in terms of pricing when agents are consuming the datadog platform to a higher degree than engineers do today

Speaker 2: Yeah. Look, I think one thing I'd say is it's hard to tell where we're gonna be in four or five years. You know, if you had told me two years ago that most engineers would go back to coding in the console, I would not believe you. Yet, you know, that's one of the winning modalities today. Yeah. yeah Look, I think one thing I'd say is it's hard to tell where we're gonna be in four or five years. look i think one thing i'd say is it's hard to tell where we're gonna be in four or five years You know, if you had told me two years ago that most engineers would go back to coding in the console, I would not believe you. you know if you had told me two years ago that most engineers would go back to coding in the console i would not believe you Yet, you know, that's one of the winning modalities today. yet you know that's one of the winning modalities today Look, as far as we're concerned, we don't care whether most of the usage is humans, most of the usage is agents. Our business model lends itself to it pretty well. Like, we're usage-based, it doesn't really matter where the usage is coming from that perspective. The way we see trends sum up right now is we see both a stratospheric increase of agent usage. Look, as far as we're concerned, we don't care whether most of the usage is humans, most of the usage is agents. look as far as we're concerned we don't care whether most of the usage is humans most of the usage is agents Our business model lends itself to it pretty well. our business model lends itself to it pretty well Like, we're usage-based, it doesn't really matter where the usage is coming from that perspective. like we're usage-based it doesn't really matter where the usage is coming from that perspective The way we see trends sum up right now is we see both a stratospheric increase of agent usage. the way we see trends sum up right now is we see both a stratospheric increase of agent usage We have a ton of usage on our MCP Server. We see customers trying to automate a lot with their own agents, using our agents, using a combination of those. We also see an increase of usage of the web interfaces by humans. Right now, the two work hand in hand, and we keep developing and pushing on both fronts. We have a ton of usage on our MCP Server. we have a ton of usage on our mcp server We see customers trying to automate a lot with their own agents, using our agents, using a combination of those. we see customers trying to automate a lot with their own agents using our agents using a combination of those We also see an increase of usage of the web interfaces by humans. we also see an increase of usage of the web interfaces by humans Right now, the two work hand in hand, and we keep developing and pushing on both fronts. right now the two work hand in hand and we keep developing and pushing on both fronts

Speaker 7: Appreciate the thoughts. Thank you. Appreciate the thoughts. appreciate the thoughts Thank you. thank you

Speaker 3: Thank you. One moment for the next question. Next question is coming from the line of Raimo Lenschow of Barclays. Please go ahead. Thank you. thank you One moment for the next question. one moment for the next question Next question is coming from the line of Raimo Lenschow of Barclays. next question is coming from the line of raimo lenschow of barclays Please go ahead. please go ahead

Speaker 8: Hey, thanks, and congrats from me as well. One for Olivier and for David. Hey, thanks, and congrats from me as well. hey thanks and congrats from me as well One for Olivier and for David. one for olivier and for david

Speaker 2: Yeah. Yeah. yeah

Speaker 8: If I listen to you and to your prepared remarks, there's a lot of, like, consolidation that people try to do open source tooling and then realize they kind of needed to come to you and come back. On the other hand, in the industry, we still have a lot of, like, noise around that level. If I listen to you and to your prepared remarks, there's a lot of, like, consolidation that people try to do open source tooling and then realize they kind of needed to come to you and come back. if i listen to you and to your prepared remarks there's a lot of like consolidation that people try to do open source tooling and then realize they kind of needed to come to you and come back On the other hand, in the industry, we still have a lot of, like, noise around that level. on the other hand in the industry we still have a lot of like noise around that level You know, how do you see it in real life? To me, it seems a little bit like observability is just very hot and then, you know, there's different categories where you use certain vendors and some open source tooling. Can you speak what you see in real life there? Thank you. You know, how do you see it in real life? you know how do you see it in real life To me, it seems a little bit like observability is just very hot and then, you know, there's different categories where you use certain vendors and some open source tooling. to me it seems a little bit like observability is just very hot and then you know there's different categories where you use certain vendors and some open source tooling Can you speak what you see in real life there? can you speak what you see in real life there Thank you. thank you

Speaker 2: I mean, in real life, you know, most companies have open source in some capacity somewhere. When it comes to having a platform that, you know, unifies everything, takes care of everything, does more of the problem-solving for you, that's, that, you know, that's typically why customers use us, you know. I mean, in real life, you know, most companies have open source in some capacity somewhere. i mean in real life you know most companies have open source in some capacity somewhere When it comes to having a platform that, you know, unifies everything, takes care of everything, does more of the problem-solving for you, that's, that, you know, that's typically why customers use us, you know. when it comes to having a platform that you know unifies everything takes care of everything does more of the problem-solving for you that's that you know that's typically why customers use us you know The motion we see pretty much, you know, everywhere is customers have four, six, seven, 15, 25 different things and different pockets in the organizations and different business units, and it's a, it's a huge mess. They come to us so they can unify all that. They get better results because all of the data is in one place. The workflows can be automated from end-to-end. You can get end-to-end visibility. You don't have blind spots. The motion we see pretty much, you know, everywhere is customers have four, six, seven, 15, 25 different things and different pockets in the organizations and different business units, and it's a, it's a huge mess. the motion we see pretty much you know everywhere is customers have four six seven 15 25 different things and different pockets in the organizations and different business units and it's a it's a huge mess They come to us so they can unify all that. they come to us so they can unify all that They get better results because all of the data is in one place. they get better results because all of the data is in one place The workflows can be automated from end- to- end. the workflows can be automated from end- to- end You can get end-to-end visibility. you can get end-to-end visibility You don't have blind spots. you don't have blind spots Also, they save money because they don't have all these pockets in inefficiency everywhere. It's a win, you know, for everyone. The thing that's also interesting in particular this quarter is that we also landed some large parts of hyperscalers. Hyperscalers typically have a culture of building everything themselves. Also, they save money because they don't have all these pockets in inefficiency everywhere. also they save money because they don't have all these pockets in inefficiency everywhere I t's a win, you know, for everyone. i t's a win you know for everyone The thing that's also interesting in particular this quarter is that we also landed some large parts of hyperscalers. the thing that's also interesting in particular this quarter is that we also landed some large parts of hyperscalers Hyperscalers typically have a culture of building everything themselves. hyperscalers typically have a culture of building everything themselves You know, they certainly have the balance sheet and the human capital to support, you know, some of that build-out. Like, if there was ever a set of companies for whom it makes sense to do it themselves, that would be those companies. Yet, you know, we see that they have the same issues. You know, when it comes to going as fast as they can, being as efficient as they can with their resources, like, they come to us to replace some of the things they were using before. You know, they certainly have the balance sheet and the human capital to support, you know, some of that build-out. you know they certainly have the balance sheet and the human capital to support you know some of that build-out Like, if there was ever a set of companies for whom it makes sense to do it themselves, that would be those companies. like if there was ever a set of companies for whom it makes sense to do it themselves that would be those companies Yet, you know, we see that they have the same issues. yet you know we see that they have the same issues You know, when it comes to going as fast as they can, being as efficient as they can with their resources, like, they come to us to replace some of the things they were using before. you know when it comes to going as fast as they can being as efficient as they can with their resources like they come to us to replace some of the things they were using before

Speaker 1: Two things, two metrics to look at that to make the points, Oli, you're making. If you look at our platform adoption and you see both the growth of the different categories and the extension of the categories out to lots of pro-products, that shows you that the consolidation on the Datadog platform, you know, has continued and is a very strong trend. Part of that is the movement of solutions, as Oli had mentioned, that are both open source, but also the competitive point solutions onto the platform. That's been a significant driver of the revenue growth for some time now, and that continued certainly in Q1. Two things, two metrics to look at that to make the points, Oli, you're making. two things two metrics to look at that to make the points oli you're making If you look at our platform adoption and you see both the growth of the different categories and the extension of the categories out to lots of pro-products, that shows you that the consolidation on the Datadog platform, you know, has continued and is a very strong trend. if you look at our platform adoption and you see both the growth of the different categories and the extension of the categories out to lots of pro-products that shows you that the consolidation on the datadog platform you know has continued and is a very strong trend Part of that is the movement of solutions, as Oli had mentioned, that are both open source, but also the competitive point solutions onto the platform. part of that is the movement of solutions as oli had mentioned that are both open source but also the competitive point solutions onto the platform That's been a significant driver of the revenue growth for some time now, and that continued certainly in Q1. that's been a significant driver of the revenue growth for some time now and that continued certainly in q1

Speaker 8: Okay. Perfect. Thank you. David, for you, the last year, we did a lot of investments around go-to-market, especially on, in sales capacity. If you think about now the non-AI category doing better, how much of that is people doing the cloud migrations again, so that's an industry trend? How much of that is you guys actually being broader positioned? Thank you. Okay. okay Perfect. perfect Thank you. thank you David, for you, the last year, we did a lot of investments around go-to-market, especially on, in sales capacity. david for you the last year we did a lot of investments around go-to-market especially on in sales capacity If you think about now the non-AI category doing better, how much of that is people doing the cloud migrations again, so that's an industry trend? if you think about now the non-ai category doing better how much of that is people doing the cloud migrations again so that's an industry trend How much of that is you guys actually being broader positioned? how much of that is you guys actually being broader positioned Thank you. thank you

Speaker 1: Yeah. Well, it's a number of things, including, one is the expansion of the platform, the successful ramping of sales capacity, while not jeopardizing productivity, which has resulted in increasing ARR and, you know, a good environment as well. I think that's what we said last time. There are a number of factors, and certainly what we're proving out here is the investments we made in go-to market and are continuing are paying off and, were the right decision. Oli, anything to add? Yeah. yeah Well, it's a number of things, including, one is the expansion of the platform, the successful ramping of sales capacity, while not jeopardizing productivity, which has resulted in increasing ARR and, you know, a good environment as well. well it's a number of things including one is the expansion of the platform the successful ramping of sales capacity while not jeopardizing productivity which has resulted in increasing arr and you know a good environment as well I think that's what we said last time. i think that's what we said last time There are a number of factors, and certainly what we're proving out here is the investments we made in go-to market and are continuing are paying off and, were the right decision. there are a number of factors and certainly what we're proving out here is the investments we made in go-to market and are continuing are paying off and were the right decision Oli, anything to add? oli anything to add

Speaker 2: Yeah. Look, at the end of the day, there's clearly some market tailwinds with the adoption of AI. Also we are outperforming all of our competitors at scale, and we're taking share. That relates to the structural platform, the way we expand with new products, the way these products are maturing and starting to win in their respective categories, and the way we've successfully grown the sales capacity. Yeah. yeah Look, at the end of the day, there's clearly some market tailwinds with the adoption of AI. look at the end of the day there's clearly some market tailwinds with the adoption of ai Also we are outperforming all of our competitors at scale, and we're taking share. also we are outperforming all of our competitors at scale and we're taking share That relates to the structural platform, the way we expand with new products, the way these products are maturing and starting to win in their respective categories, and the way we've successfully grown the sales capacity. that relates to the structural platform the way we expand with new products the way these products are maturing and starting to win in their respective categories and the way we've successfully grown the sales capacity

Speaker 1: Certainly AI, the AI investment trend has helped, but what we're trying to do is separate that, and AI investment is probably helping the overall as well. When you really take that out, you see a very pronounced acceleration here, and that has to do with the factors that I mentioned and Oli talked about. Certainly AI, the AI investment trend has helped, but what we're trying to do is separate that, a nd AI investment is probably helping the overall as well. certainly ai the ai investment trend has helped but what we're trying to do is separate that, a nd ai investment is probably helping the overall as well When you really take that out, you see a very pronounced acceleration here, and that has to do with the factors that I mentioned and Oli talked about. when you really take that out you see a very pronounced acceleration here and that has to do with the factors that i mentioned and oli talked about

Speaker 8: Perfect. Congrats. Sounds exciting. Perfect. perfect Congrats. congrats Sounds exciting. sounds exciting

Speaker 1: Thanks. Thanks. thanks

Speaker 3: One moment please for the next question. Our next question is coming from the line of Gabriela Borges of Goldman Sachs. Please go ahead. One moment please for the next question. one moment please for the next question Our next question is coming from the line of Gabriela Borges of Goldman Sachs. our next question is coming from the line of gabriela borges of goldman sachs Please go ahead. please go ahead

Speaker 9: Hi. Good morning. Thank you. Olivier, I find your comments on training versus inference so interesting. Maybe just crystallize for us, why do you think the training opportunity is happening now or inflecting now? And then either for yourself or David, how do we think about the attach rate on training versus inference of observability? If there is a way to benchmark observability spend as a percentage of inference spend, does that number change given the new data that you're seeing on the training side as well? Thank you so much. Hi. hi Good morning. good morning Thank you. thank you Olivier, I find your comments on training versus inference so interesting. olivier i find your comments on training versus inference so interesting Maybe just crystallize for us, why do you think the training opportunity is happening now or inflecting now? maybe just crystallize for us why do you think the training opportunity is happening now or inflecting now And then either for yourself or David, how do we think about the attach rate on training versus inference of observability? and then either for yourself or david how do we think about the attach rate on training versus inference of observability If there is a way to benchmark observability spend as a percentage of inference spend, does that number change given the new data that you're seeing on the training side as well? if there is a way to benchmark observability spend as a percentage of inference spend does that number change given the new data that you're seeing on the training side as well Thank you so much. thank you so much

Speaker 2: On the training side, training was very new a couple of years ago. It was something that was only done by very few companies, and it was, in a way, very artisanal. It was not a production workload, it was something that researchers were building, and that was very one-off and homegrown in ways. Now it's turning into production. It's turning into something that many more companies are doing. It's scaling by orders of magnitude, and it's becoming something that has to be on all the time, reliable and, you know, every minute you lose is a or rather every failure you have in your training run is a week you give away to the competition. On the training side, training was very new a couple of years ago. on the training side training was very new a couple of years ago It was something that was only done by very few companies, and it was, in a way, very artisanal. it was something that was only done by very few companies and it was in a way very artisanal It was not a production workload, it was something that researchers were building, and that was very one-off and homegrown in ways. it was not a production workload it was something that researchers were building and that was very one-off and homegrown in ways Now it's turning into production. now it's turning into production It's turning into something that many more companies are doing. it's turning into something that many more companies are doing It's scaling by orders of magnitude, and it's becoming something that has to be on all the time, reliable and, you know, every minute you lose is a or rather every failure you have in your training run is a week you give away to the competition. it's scaling by orders of magnitude and it's becoming something that has to be on all the time reliable and you know every minute you lose is a or rather every failure you have in your training run is a week you give away to the competition As a result, you know, it becomes way more interesting as a, as a market for companies like us. We see some signs of that. You know, again, we didn't have a lot of it. We didn't see a lot of it last year. Now all of a sudden we're starting to see quite a bit of activity there and demand, and we have success landing with large customers with those products. As a result, you know, it becomes way more interesting as a, as a market for companies like us. as a result you know it becomes way more interesting as a as a market for companies like us We see some signs of that. we see some signs of that You know, again, we didn't have a lot of it. you know again we didn't have a lot of it We didn't see a lot of it last year. we didn't see a lot of it last year Now all of a sudden we're starting to see quite a bit of activity there and demand, and we have success landing with large customers with those products. now all of a sudden we're starting to see quite a bit of activity there and demand and we have success landing with large customers with those products

Speaker 1: Yeah. I think going back to the metrics that Oli talked about, you know, in terms of attach, we said that 6,500 customers are using our integrations, and that's 20% of the customers and 80% of the ARR. There is attach. I think it's earlier days for the training, you know, all that looks like it will be a contributor. I think we- that's early and I would sort of look at the larger attachment at this point as the evidence of inference but also some training. Yeah. yeah I think going back to the metrics that Oli talked about, you know, in terms of attach, we said that 6,500 customers are using our integrations, and that's 20% of the customers and 80% of the ARR. i think going back to the metrics that oli talked about you know in terms of attach we said that 6,500 customers are using our integrations and that's 20% of the customers and 80% of the arr There is attach. there is attach I think it's earlier days for the training, you know, all that looks like it will be a contributor. i think it's earlier days for the training you know all that looks like it will be a contributor I think we- that's early and I would sort of look at the larger attachment at this point as the evidence of inference but also some training. i think we- that's early and i would sort of look at the larger attachment at this point as the evidence of inference but also some training

Speaker 9: Thank you both, and congratulations. Thank you both, and congratulations. thank you both and congratulations

Speaker 1: Thank you. Thank you. thank you

Speaker 3: Thank you. One moment please for the next question. Our next question is coming from the line of Karl Keirstead of UBS. Please go ahead. Thank you. thank you One moment please for the next question. one moment please for the next question Our next question is coming from the line of Karl Keirstead of UBS. our next question is coming from the line of karl keirstead of ubs Please go ahead. please go ahead

Speaker 10: Okay, great. I wanted to start, Olivier and David and you congratulating all of you and the team on reaching that billion-dollar milestone. Well done. David, maybe the question is for you and to hone in specifically on the 2Q guide. Even if you put up a modest beat on that guide, it's going to be, you know, by order of magnitude, the largest sequential dollar add, I think, in the company's history. Okay, great. okay great I wanted to start, Olivier and David and you congratulating all of you and the team on reaching that billion-dollar milestone. i wanted to start olivier and david and you congratulating all of you and the team on reaching that billion-dollar milestone Well done. well done David, maybe the question is for you and to hone in specifically on the 2Q guide. david maybe the question is for you and to hone in specifically on the 2q guide Even if you put up a modest beat on that guide, it's going to be, you know, by order of magnitude, the largest sequential dollar add, I think, in the company's history. even if you put up a modest beat on that guide it's going to be you know by order of magnitude the largest sequential dollar add i think in the company's history I just wanted to unpack what's giving you that confidence. In particular, is there anything interesting to call out, David, in terms of the ramp of a couple of the larger research labs, one of which renewed with you guys in the fourth quarter, another one just landed. I presume they're ramping nicely in 2Q, but would love any color. Thank you. I just wanted to unpack what's giving you that confidence. i just wanted to unpack what's giving you that confidence In particular, is there anything interesting to call out, David, in terms of the ramp of a couple of the larger research labs, one of which renewed with you guys in the fourth quarter, another one just landed. in particular is there anything interesting to call out david in terms of the ramp of a couple of the larger research labs one of which renewed with you guys in the fourth quarter another one just landed I presume they're ramping nicely in 2Q, but would love any color. i presume they're ramping nicely in 2q but would love any color Thank you. thank you

Speaker 1: Yeah. Let me unpack this in a couple ways. As you know, we're a recurring revenue model. The biggest indication of in the near term or the next quarter is the ARR growth in the previous quarter, and when we said we had a record. Essentially, at the bedrock of this is sort of the run forward of ARR that we've already signed. Yeah. yeah Let me unpack this in a couple ways. let me unpack this in a couple ways As you know, we're a recurring revenue model. as you know we're a recurring revenue model The biggest indication of in the near term or the next quarter is the ARR growth in the previous quarter, and when we said we had a record. the biggest indication of in the near term or the next quarter is the arr growth in the previous quarter and when we said we had a record Essentially, at the bedrock of this is sort of the run forward of ARR that we've already signed. essentially at the bedrock of this is sort of the run forward of arr that we've already signed The ARR add was very broad-based and was not very concentrated. Whereas we pointed out some very significant adds, I would say that the first quarter and that ARR add was really diversified and from lots of different places. I think Oli will come in here, but the confidence that we have is, you're right, we essentially take what we already have. The ARR add was very broad-based and was not very concentrated. the arr add was very broad-based and was not very concentrated Whereas we pointed out some very significant adds, I would say that the first quarter and that ARR add was really diversified and from lots of different places. whereas we pointed out some very significant adds i would say that the first quarter and that arr add was really diversified and from lots of different places I think Oli will come in here, but the confidence that we have is, you're right, we essentially take what we already have. i think oli will come in here but the confidence that we have is you're right we essentially take what we already have We discount the growth trends that we've seen, and that produces what you exactly said, which is, you know, whatever your assumptions are on beat, you know, a very impressive sequential, really due to what happened in Q1 and the rate of business accumulation by Datadog. Oli, do you wanna add? We discount the growth trends that we've seen, and that produces what you exactly said, which is, you know, whatever your assumptions are on beat, you know, a very impressive sequential, really due to what happened in Q1 and the rate of business accumulation by Datadog. we discount the growth trends that we've seen and that produces what you exactly said which is you know whatever your assumptions are on beat you know a very impressive sequential really due to what happened in q1 and the rate of business accumulation by datadog Oli, do you wanna add? oli do you wanna add

Speaker 2: Yeah, I would. I mean, I basically want to develop on what David just said. The adds were broad-based. Why we have a great Q1? We have also landed great customers in Q4. We had talked about it a quarter ago. Even if you take the, if you take out the customer we land in Q4 that added the most revenue in Q1, we still had a record quarter in terms of ARR adds. This is really broad-based. We landed a few more customers in Q1 that don't contribute any revenue yet, but we expect to be big contributors in the future. When you put all that together, we feel very confident about Q2, hence the numbers you're seeing. Yeah, I would. yeah i would I mean, I basically want to develop on what David just said. i mean i basically want to develop on what david just said The adds were broad-based. the adds were broad-based Why we have a great Q1? why we have a great q1 We have also landed great customers in Q4. we have also landed great customers in q4 We had talked about it a quarter ago. we had talked about it a quarter ago Even if you take the, if you take out the customer we land in Q4 that added the most revenue in Q1, we still had a record quarter in terms of ARR adds. even if you take the if you take out the customer we land in q4 that added the most revenue in q1 we still had a record quarter in terms of arr adds This is really broad-based. this is really broad-based We landed a few more customers in Q1 that don't contribute any revenue yet, but we expect to be big contributors in the future. we landed a few more customers in q1 that don't contribute any revenue yet but we expect to be big contributors in the future When you put all that together, we feel very confident about Q2, hence the numbers you're seeing. when you put all that together we feel very confident about q2 hence the numbers you're seeing

Speaker 10: Yeah. Thank you. Yeah. yeah Thank you. thank you

Speaker 3: Thank you. One moment, please. Our next question will be coming from the line of Fatima Boolani of Citi. Please go ahead. Thank you. thank you One moment, please. one moment please Our next question will be coming from the line of Fatima Boolani of Citi. our next question will be coming from the line of fatima boolani of citi Please go ahead. please go ahead

Speaker 11: Good morning. Thank you for taking my questions. Oli, I wanted to double back on a question that was asked earlier with respect to telemetry volumes, you know, essentially going parabolic, and you are accessing brand new demand vectors, you know, in the foray into training and monitoring, observing training model environments inside some of the world's largest frontier labs. I wanted to ask you about the structural changes to the capital intensity of the business. I mean, your CapEx levels are still pretty respectable and pretty muted. Good morning. good morning Thank you for taking my questions. thank you for taking my questions Oli, I wanted to double back on a question that was asked earlier with respect to telemetry volumes, you know, essentially going parabolic, and you are accessing brand new demand vectors, you know, in the foray into training and monitoring, observing training model environments inside some of the world's largest frontier labs. oli i wanted to double back on a question that was asked earlier with respect to telemetry volumes you know essentially going parabolic and you are accessing brand new demand vectors you know in the foray into training and monitoring observing training model environments inside some of the world's largest frontier labs I wanted to ask you about the structural changes to the capital intensity of the business. i wanted to ask you about the structural changes to the capital intensity of the business I mean, your CapEx levels are still pretty respectable and pretty muted. i mean your capex levels are still pretty respectable and pretty muted I wanted to get a better understanding of what sort of extrinsic or intrinsic engineering efforts you're undertaking to keep a very efficient CapEx envelope, in spite of the fact that it seems like that would increase because of the torrent of telemetry you're seeing on the platform. As a related matter, we've seen a rise of sovereign data and data residency requirements kind of ramp as AI models, you know, move into the territory of national security and things like that. I wanted to get a better understanding of what sort of extrinsic or intrinsic engineering efforts you're undertaking to keep a very efficient CapEx envelope, in spite of the fact that it seems like that would increase because of the torrent of telemetry you're seeing on the platform. i wanted to get a better understanding of what sort of extrinsic or intrinsic engineering efforts you're undertaking to keep a very efficient capex envelope in spite of the fact that it seems like that would increase because of the torrent of telemetry you're seeing on the platform As a related matter, we've seen a rise of sovereign data and data residency requirements kind of ramp as AI models, you know, move into the territory of national security and things like that. as a related matter we've seen a rise of sovereign data and data residency requirements kind of ramp as ai models you know move into the territory of national security and things like that Wondering if you can kind of talk to some of the engineering horsepower internally that you're leveraging to be able to keep a really tight command on capital intensity and frankly, your gross margins. Thank you. Wondering if you can kind of talk to some of the engineering horsepower internally that you're leveraging to be able to keep a really tight command on capital intensity and frankly, your gross margins. wondering if you can kind of talk to some of the engineering horsepower internally that you're leveraging to be able to keep a really tight command on capital intensity and frankly your gross margins Thank you. thank you

Speaker 2: Yeah. I mean, look, the investments we're making right now, we run most of our workloads on clouds, meaning you'll see all of that in OpEx, not in CapEx. We have low CapEx, you know. If it changes, we'll tell you. Like if for some reason we decide to make different kinds of investments and some of it more up-front, some of it more CapEx, we'll tell you, but that's not the case today. Yeah. yeah I mean, look, the investments we're making right now, we run most of our workloads on clouds, meaning you'll see all of that in OpEx, not in CapEx. i mean look the investments we're making right now we run most of our workloads on clouds meaning you'll see all of that in opex not in capex We have low CapEx, you know. we have low capex you know If it changes, we'll tell you. if it changes we'll tell you Like if for some reason we decide to make different kinds of investments and some of it more up-front, some of it more CapEx, we'll tell you, but that's not the case today. like if for some reason we decide to make different kinds of investments and some of it more up-front some of it more capex we'll tell you but that's not the case today We are definitely ramping up our investments in particular in R&D, and in the scale of the models we train ourselves and things like that. Right now, there's nothing that you can actually see in the numbers that moves any needle, but you know, if that changes, also we'll tell you. We don't expect any change to our model. We are definitely ramping up our investments in particular in R&D, and in the scale of the models we train ourselves and things like that. we are definitely ramping up our investments in particular in r&d and in the scale of the models we train ourselves and things like that Right now, there's nothing that you can actually see in the numbers that moves any needle, but you know, if that changes, also we'll tell you. right now there's nothing that you can actually see in the numbers that moves any needle but you know if that changes also we'll tell you We don't expect any change to our model. we don't expect any change to our model That's on the CapEx side. We're very different businesses in that way from the AI labs. On the subject of data residency and sovereignty of AI, you know, and things like that, we definitely see more push for that, more demand for that in the customer base. For us, that means investments in two areas. That's on the CapEx side. that's on the capex side We're very different businesses in that way from the AI labs. we're very different businesses in that way from the ai labs On the subject of data residency and sovereignty of AI, you know, and things like that, we definitely see more push for that, more demand for that in the customer base. on the subject of data residency and sovereignty of ai you know and things like that we definitely see more push for that more demand for that in the customer base For us, that means investments in two areas. for us that means investments in two areas One is in deploying into more geographies and having more certifications to sell to the public sector and to the highest levels of the public sector. You know, we mentioned today data center in the U.K., for example, or our FedRAMP High certification. We're not stopping there, you know, in terms of the certification we're going after with the federal government. That's an area of investment. One is in deploying into more geographies and having more certifications to sell to the public sector and to the highest levels of the public sector. one is in deploying into more geographies and having more certifications to sell to the public sector and to the highest levels of the public sector You know, we mentioned today data center in the U.K., for example, or our FedRAMP High certification. you know we mentioned today data center in the u.k for example or our fedramp high certification We're not stopping there, you know, in terms of the certification we're going after with the federal government. we're not stopping there you know in terms of the certification we're going after with the federal government That's an area of investment. that's an area of investment Another area of investment is our Bring Your Own Cloud products, where we can actually run on our customers' infrastructure. We announced that. We released some products there, and we have heavy investment in that area, so we can support customers that want to operate in a slightly separate way from the rest of our customer base. Another area of investment is our Bring Your Own Cloud products, where we can actually run on our customers' infrastructure. another area of investment is our bring your own cloud products where we can actually run on our customers' infrastructure We announced that. we announced that We released some products there, and we have heavy investment in that area, so we can support customers that want to operate in a slightly separate way from the rest of our customer base. we released some products there and we have heavy investment in that area so we can support customers that want to operate in a slightly separate way from the rest of our customer base

Speaker 11: Thank you. Thank you. thank you

Speaker 3: Thank you. One moment for the next question. Our next question is coming from the line of Kirk Materne of Evercore. Please go ahead. Thank you. thank you One moment for the next question. one moment for the next question Our next question is coming from the line of Kirk Materne of Evercore. our next question is coming from the line of kirk materne of evercore Please go ahead. please go ahead

Speaker 5: Yeah. Thanks very much for taking the question, and congrats on a nice start. Oli, I was wondering if you could just give some thoughts on the idea of sort of security for agents. I think one of the big, you know, issues in terms of getting agents into production, you know, is sort of the security aspect of that. How do you see Datadog plugging into that opportunity? Yeah. yeah Thanks very much for taking the question, and congrats on a nice start. thanks very much for taking the question and congrats on a nice start Oli, I was wondering if you could just give some thoughts on the idea of sort of security for agents. oli i was wondering if you could just give some thoughts on the idea of sort of security for agents I think one of the big, you know, issues in terms of getting agents into production, you know, is sort of the security aspect of that. i think one of the big you know issues in terms of getting agents into production you know is sort of the security aspect of that How do you see Datadog plugging into that opportunity? how do you see datadog plugging into that opportunity Just a quick one for David. Congrats on the FedRAMP, you know, reaching that milestone. You know, are your partner relationships in place to take advantage of this? I realize it'll be a, you know, long-term opportunity, but just kinda curious how well established you are down there to start seeing some maybe bookings in that area. Thanks, guys. Just a quick one for David. just a quick one for david Congrats on the FedRAMP, you know, reaching that milestone. congrats on the fedramp you know reaching that milestone You know, are your partner relationships in place to take advantage of this? you know are your partner relationships in place to take advantage of this I realize it'll be a, you know, long-term opportunity, but just kinda curious how well established you are down there to start seeing some maybe bookings in that area. i realize it'll be a you know long-term opportunity but just kinda curious how well established you are down there to start seeing some maybe bookings in that area Thanks, guys. thanks guys

Speaker 2: Yeah. On the security of agents, we interface with that in two ways. First, there's the agents we build ourselves because we are building a lot of automation inside of our product for our customers and agents that automatically identify but also resolve issues without you having to do anything. Yeah. yeah On the security of agents, we interface with that in two ways. on the security of agents we interface with that in two ways First, there's the agents we build ourselves because we are building a lot of automation inside of our product for our customers and agents that automatically identify but also resolve issues without you having to do anything. first there's the agents we build ourselves because we are building a lot of automation inside of our product for our customers and agents that automatically identify but also resolve issues without you having to do anything There, a lot of it has to do with understanding what permissions to apply, what kind of guardrails to apply, how to interface with the humans, and you know, how to make that trustworthy and visible in the right way. That's pretty much the whole product surface is figuring that out. The automation itself actually kind of works already. You should expect to hear more about that at our conference. There, a lot of it has to do with understanding what permissions to apply, what kind of guardrails to apply, how to interface with the humans, and you know, how to make that trustworthy and visible in the right way. there a lot of it has to do with understanding what permissions to apply what kind of guardrails to apply how to interface with the humans and you know how to make that trustworthy and visible in the right way That's pretty much the whole product surface is figuring that out. that's pretty much the whole product surface is figuring that out The automation itself actually kind of works already. the automation itself actually kind of works already You should expect to hear more about that at our conference. you should expect to hear more about that at our conference This is definitely one big area of investment for us. The security aspects of running agents. Look, we, our belief in security is that you need to integrate. You can't just have point solutions that look at one sliver of the whole security posture. You need to look at everything all together. That's one of the areas that we are also covering with our security efforts. That's part of the whole platform actually. This is definitely one big area of investment for us. this is definitely one big area of investment for us The security aspects of running agents. the security aspects of running agents Look, we, our belief in security is that you need to integrate. look we our belief in security is that you need to integrate You can't just have point solutions that look at one sliver of the whole security posture. you can't just have point solutions that look at one sliver of the whole security posture You need to look at everything all together. you need to look at everything all together That's one of the areas that we are also covering with our security efforts. that's one of the areas that we are also covering with our security efforts That's part of the whole platform actually. that's part of the whole platform actually

Speaker 1: On the FedRAMP. We've been working on both the different certifications, but at the same time, we've been investing in the go-to-market function, both in terms of reps and channel partners for a number of years. Certainly, there's more investment to be done, but we invested ahead of the certifications because, you know, in this sector, building pipeline, et cetera, takes time. Certainly, the channel partner relationships are a very important part of this, and we have been investing, but also have more investment to do. On the FedRAMP. on the fedramp We've been working on both the different certifications, but at the same time, we've been investing in the go-to-market function, both in terms of reps and channel partners for a number of years. we've been working on both the different certifications but at the same time we've been investing in the go-to-market function both in terms of reps and channel partners for a number of years Certainly, there's more investment to be done, but we invested ahead of the certifications because, you know, in this sector, building pipeline, et cetera, takes time. certainly there's more investment to be done but we invested ahead of the certifications because you know in this sector building pipeline et cetera takes time Certainly, the channel partner relationships are a very important part of this, and we have been investing, but also have more investment to do. certainly the channel partner relationships are a very important part of this and we have been investing but also have more investment to do

Speaker 5: Thank you. Thank you. thank you

Speaker 3: One moment, please, for the next question. Our next question is coming from the line of Patrick Colville of Scotiabank. Please go ahead. One moment, please, for the next question. one moment please for the next question Our next question is coming from the line of Patrick Colville of Scotiabank. our next question is coming from the line of patrick colville of scotiabank Please go ahead. please go ahead

Speaker 12: Thank you for taking my question and echoing the congrats of my peers. I guess, you know, Olivier and David, you guys are very deliberate in your messaging on the prepared remarks, I just wanna double-check the kind of wording of one of the comments. I think, David, you said higher degree of conservatism to the largest customer. Thank you for taking my question and echoing the congrats of my peers. thank you for taking my question and echoing the congrats of my peers I guess, you know, Olivier and David, you guys are very deliberate in your messaging on the prepared remarks, I just wanna double-check the kind of wording of one of the comments. i guess you know olivier and david you guys are very deliberate in your messaging on the prepared remarks i just wanna double-check the kind of wording of one of the comments I think, David, you said higher degree of conservatism to the largest customer. i think david you said higher degree of conservatism to the largest customer I guess, did I hear that right? Does the higher degree of conservatism reference versus the other customer cohorts, or does it reference versus your guidance philosophy in prior quarters vis-à-vis this customer? I guess, did I hear that right? i guess did i hear that right Does the higher degree of conservatism reference versus the other customer cohorts, or does it reference versus your guidance philosophy in prior quarters vis-à-vis this customer? does the higher degree of conservatism reference versus the other customer cohorts or does it reference versus your guidance philosophy in prior quarters vis-à-vis this customer

Speaker 1: It's both. It's the same guidance we used. We're being very explicit. For all the business except for this largest customers, we've always taken the drivers and discounted them. For this particular customer, we took a higher degree of conservatism than the other part of the customer base and discounted it more. We were, I think, in the remarks, you interpreted correct, very explicit, and you're correct. It's both. it's both It's the same guidance we used. it's the same guidance we used We're being very explicit. we're being very explicit For all the business except for this largest customers, we've always taken the drivers and discounted them. for all the business except for this largest customers we've always taken the drivers and discounted them For this particular customer, we took a higher degree of conservatism than the other part of the customer base and discounted it more. for this particular customer we took a higher degree of conservatism than the other part of the customer base and discounted it more We were, I think, in the remarks, you interpreted correct, very explicit, and you're correct. we were i think in the remarks you interpreted correct very explicit and you're correct

Speaker 2: I would, you know, give that much weight to the very specific way that we're deliberate, but not all that deliberate, you know. Similarly, both David and I have a raspy voice today, but there's no hidden meaning. I would, you know, give that much weight to the very specific way that we're deliberate, but not all that deliberate, you know. i would you know give that much weight to the very specific way that we're deliberate but not all that deliberate you know Similarly, both David and I have a raspy voice today, but there's no hidden meaning. similarly both david and i have a raspy voice today but there's no hidden meaning

Speaker 1: I will remind everybody that we did not change. If the question also, I think you asked, is did we change or is this a different methodology of both the overall and the large customer than the guidance the last quarter or the previous? The answer is no. It's the same methodology, and that we've had. No change, but that's been what we've always been doing. I will remind everybody that we did not change. i will remind everybody that we did not change If the question also, I think you asked, is did we change or is this a different methodology of both the overall and the large customer than the guidance the last quarter or the previous? if the question also i think you asked is did we change or is this a different methodology of both the overall and the large customer than the guidance the last quarter or the previous The answer is no. the answer is no It's the same methodology, and that we've had. it's the same methodology and that we've had No change, but that's been what we've always been doing. no change but that's been what we've always been doing

Speaker 12: Okay, and Olivier, can I ask about your comments about the hyperscalers? 'Cause I thought that was particularly interesting. The reason why is I don't think you called them out previously before, and, you know, they are so prevalent in the modern tech stack. To your point, they could do this themselves. I guess how are they using Datadog? Is it for more kind of traditional observability, or is it for these newer areas like GPU Monitoring that Datadog has performed so well in, you know, of late? Okay, a nd Olivier, can I ask about your comments about the hyperscalers? 'Cause I thought that was particularly interesting. okay, a nd olivier can i ask about your comments about the hyperscalers 'cause i thought that was particularly interesting The reason why is I don't think you called them out previously before, and, you know, they are so prevalent in the modern tech stack. the reason why is i don't think you called them out previously before and you know they are so prevalent in the modern tech stack To your point, they could do this themselves. to your point they could do this themselves I guess how are they using Datadog? i guess how are they using datadog Is it for more kind of traditional observability, or is it for these newer areas like GPU Monitoring that Datadog has performed so well in, you know, of late? is it for more kind of traditional observability or is it for these newer areas like gpu monitoring that datadog has performed so well in you know of late

Speaker 2: Well, it's both actually. When you look in general at the large AI customers, they use Datadog the way other companies are largely with a fairly broad set of our products to cover the full surface of observability. What's new is we now have a product for GPU Monitoring. It's a very new product. We see the hyperscalers that are coming to us for training workloads in particular, being very interested in that. Again, it's too early in the product life cycle and the customer life cycle for these specific customers to call definitive victory there. Well, it's both actually. well it's both actually When you look in general at the large AI customers, they use Datadog the way other companies are largely with a fairly broad set of our products to cover the full surface of observability. when you look in general at the large ai customers they use datadog the way other companies are largely with a fairly broad set of our products to cover the full surface of observability What's new is we now have a product for GPU Monitoring. what's new is we now have a product for gpu monitoring It's a very new product. it's a very new product We see the hyperscalers that are coming to us for training workloads in particular, being very interested in that. we see the hyperscalers that are coming to us for training workloads in particular being very interested in that Again, it's too early in the product life cycle and the customer life cycle for these specific customers to call definitive victory there. again it's too early in the product life cycle and the customer life cycle for these specific customers to call definitive victory there We see that as a very encouraging sign of where the market might go in the future because we think this might be a bellwether of what, you know, the next 100, 500 companies that are going to start training workloads are going to want to do. We have some signs that go beyond the customers we signed this quarter that point that way too. We see that as a very encouraging sign of where the market might go in the future because we think this might be a bellwether of what, you know, the next 100, 500 companies that are going to start training workloads are going to want to do. we see that as a very encouraging sign of where the market might go in the future because we think this might be a bellwether of what you know the next 100 500 companies that are going to start training workloads are going to want to do We have some signs that go beyond the customers we signed this quarter that point that way too. we have some signs that go beyond the customers we signed this quarter that point that way too

Speaker 12: Thank you so much. Thank you so much. thank you so much

Speaker 3: Thank you. One moment for the next question, please. Our next question is coming from the line of Peter Weed of Bernstein Research. Please go ahead. Thank you. thank you One moment for the next question, please. one moment for the next question please Our next question is coming from the line of Peter Weed of Bernstein Research. our next question is coming from the line of peter weed of bernstein research Please go ahead. please go ahead

Speaker 13: Thank you. I'll echo others on the momentum. Great to see. You know, one of, I think, the great successes you talked about was landing a couple of the AI labs for the hyperscalers. Although I think, you know, on the other hand, you've talked in the past around, you know, hyperscalers are typically building observability in-house. Thank you. thank you I'll echo others on the momentum. i'll echo others on the momentum Great to see. great to see You know, one of, I think, the great successes you talked about was landing a couple of the AI labs for the hyperscalers. you know one of i think the great successes you talked about was landing a couple of the ai labs for the hyperscalers Although I think, you know, on the other hand, you've talked in the past around, you know, hyperscalers are typically building observability in-house. although i think you know on the other hand you've talked in the past around you know hyperscalers are typically building observability in-house What is it really about the AI workloads that are making it more attractive for them to use Datadog? What might give you confidence that Datadog might be more persistent with them in these types of workloads and as kind of a signal for maybe how other customers might use Datadog around AI differentiated from things that they might be able to bring in-house other places? What is it really about the AI workloads that are making it more attractive for them to use Datadog? what is it really about the ai workloads that are making it more attractive for them to use datadog What might give you confidence that Datadog might be more persistent with them in these types of workloads and as kind of a signal for maybe how other customers might use Datadog around AI differentiated from things that they might be able to bring in-house other places? what might give you confidence that datadog might be more persistent with them in these types of workloads and as kind of a signal for maybe how other customers might use datadog around ai differentiated from things that they might be able to bring in-house other places

Speaker 2: Well, you know, the same reason all of our customers use us, you know, it's a high stakes, high complexity, and not core. Like, you know, they have to be always differentiated. They can't afford to be late. It's a really hard job to do all of that. That's what we build our whole business on. It's also very true for at the highest level for the largest companies. Well, you know, the same reason all of our customers use us, you know, it's a high stakes, high complexity, and not core. well you know the same reason all of our customers use us you know it's a high stakes high complexity and not core Like, you know, they have to be always differentiated. like you know they have to be always differentiated They can't afford to be late. they can't afford to be late It's a really hard job to do all of that. it's a really hard job to do all of that That's what we build our whole business on. that's what we build our whole business on It's also very true for at the highest level for the largest companies. it's also very true for at the highest level for the largest companies

Speaker 3: Thank you. One moment please. Thank you. thank you One moment please. one moment please

Speaker 13: I mean, again. I mean, again. i mean again

Speaker 3: I'm sorry. I'm sorry. i'm sorry

Speaker 13: Oh, yeah. No, I was just gonna say, but I guess, you know, the point is you've emphasized that those largest customers have been able to go in-house on some other things. Is there something- Oh, yeah. oh yeah No, I was just gonna say, but I guess, you know, the point is you've emphasized that those largest customers have been able to go in-house on some other things. no i was just gonna say but i guess you know the point is you've emphasized that those largest customers have been able to go in-house on some other things Is there something- is there something-

Speaker 2: Yeah. Yeah. yeah

Speaker 13: ... unique about AI that prevents them from doing that here? ... unique about AI that prevents them from doing that here? ... unique about ai that prevents them from doing that here

Speaker 2: Well, I think the urgency of the development efforts focuses the minds. That's what I would put it, you know. I would say, you know, it forces you to figure out what's core and what's not core and what you need to do to maximize your chances of success. Again, it is a same thinking all of our customers have all the time. I think the equation for hyperscalers has often been slightly different because they have, let's call it, unlimited access to staffings. You know, they could sort of set their own time, their own time horizons for the developments they wanted to make. I think the situation is a little bit different with AI race maybe. Well, I think the urgency of the development efforts focuses the minds. well i think the urgency of the development efforts focuses the minds That's what I would put it, you know. that's what i would put it you know I would say, you know, it forces you to figure out what's core and what's not core and what you need to do to maximize your chances of success. i would say you know it forces you to figure out what's core and what's not core and what you need to do to maximize your chances of success Again, it is a same thinking all of our customers have all the time. again it is a same thinking all of our customers have all the time I think the equation for hyperscalers has often been slightly different because they have, let's call it, unlimited access to staffings. i think the equation for hyperscalers has often been slightly different because they have let's call it unlimited access to staffings You know, they could sort of set their own time, their own time horizons for the developments they wanted to make. you know they could sort of set their own time their own time horizons for the developments they wanted to make I think the situation is a little bit different with AI race maybe. i think the situation is a little bit different with ai race maybe

Speaker 13: Thank you. Thank you. thank you

Speaker 3: The line of Gregg Moskowitz of Mizuho. Please go ahead. The line of Gregg Moskowitz of Mizuho. the line of gregg moskowitz of mizuho Please go ahead. please go ahead

Speaker 14: All right. Great. Thank you. I'll add my congratulations on a terrific quarter. Just one for me. Oli, I know it's not GA yet, but curious if you have any early feedback on your new CloudPrem offering. As you noted earlier, you know, providing the ability for Datadog to run on customer infrastructure. Could this be another, yet another, I should say, incremental growth opportunity for Datadog? What are your expectations for this? All right. all right Great. great Thank you. thank you I'll add my congratulations on a terrific quarter. i'll add my congratulations on a terrific quarter Just one for me. just one for me Oli, I know it's not GA yet, but curious if you have any early feedback on your new CloudPrem offering. oli i know it's not ga yet but curious if you have any early feedback on your new cloudprem offering As you noted earlier, you know, providing the ability for Datadog to run on customer infrastructure. as you noted earlier you know providing the ability for datadog to run on customer infrastructure Could this be another, yet another, I should say, incremental growth opportunity for Datadog? could this be another yet another i should say incremental growth opportunity for datadog What are your expectations for this? what are your expectations for this

Speaker 2: Well, definitely we think, you know, as I think there was a question earlier on, data residency and, you know, living in customers environment. We definitely see a great opportunity there. You know, there's a chance that a good portion of the market, you know, leans this way in the future. You know, today it's not the largest part of the market, but we definitely see a potential for that. We're investing heavily in that side of our product. We're starting to see some interesting customer traction there, you know. We think this can be another growth lever definitely. Well, definitely we think, you know, as I think there was a question earlier on, data residency and, you know, living in customers environment. well definitely we think you know as i think there was a question earlier on data residency and you know living in customers environment We definitely see a great opportunity there. we definitely see a great opportunity there You know, there's a chance that a good portion of the market, you know, leans this way in the future. you know there's a chance that a good portion of the market you know leans this way in the future You know, today it's not the largest part of the market, but we definitely see a potential for that. you know today it's not the largest part of the market but we definitely see a potential for that We're investing heavily in that side of our product. we're investing heavily in that side of our product We're starting to see some interesting customer traction there, you know. we're starting to see some interesting customer traction there you know We think this can be another growth lever definitely. we think this can be another growth lever definitely We also think that it can help us getting into some extremely large scale workload where customers would not have considered a SaaS offering before, where we can be in the running. That's very exciting. We also think that it can help us getting into some extremely large scale workload where customers would not have considered a SaaS offering before, where we can be in the running. we also think that it can help us getting into some extremely large scale workload where customers would not have considered a saas offering before where we can be in the running That's very exciting. that's very exciting

Speaker 14: Great. Thank you. Great. great Thank you. thank you

Speaker 2: All right. I think that was our last question. I want to thank you all for attending the call, and remind you that we have a conference in just a bit more than a month, and I hope to see many of you there. Thank you all. All right. all right I think that was our last question. i think that was our last question I want to thank you all for attending the call, and remind you that we have a conference in just a bit more than a month, and I hope to see many of you there. i want to thank you all for attending the call and remind you that we have a conference in just a bit more than a month and i hope to see many of you there Thank you all. thank you all

Speaker 3: This concludes today's program. You may all disconnect. This concludes today's program. this concludes today's program You may all disconnect. you may all disconnect