Earnings Call Transcript - DDOG Q1 2026

Operator, Operator

Good day, and thank you for standing by. Welcome to the First Quarter 2026 Datadog Earnings Conference Call. Please be advised that today's conference is being recorded. I would now like to turn the conference over to Yuka Broderick, Senior Vice President of Investor Relations. Please go ahead.

Yuka Broderick, Senior Vice President, Investor Relations

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's 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 in 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.

Olivier Pomel, Co-Founder & CEO

Thanks, 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 continues to grow and diversify rapidly both in the number of customers we serve and the scale of those customers. In this quarter, including 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 the mid-20% year-over-year range, 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 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 $1.01 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 25,500 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. And 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. So we are winning 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. 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. But we are still just getting started. Of our 26 products, five are over $100 million in ARR and another three are between $50 million and $100 million ARR. We're working hard to build and deliver further growth in those products. And 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. So 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. So 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 our Bits AI Security Agent, which autonomously triages Datadog Cloud SIEM signals, conducts in-depth investigations of potential threats, and delivers actionable recommendations. We've seen the Bits AI Security Agent 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 queries. 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 data platform. We now have over 6,500 customers sending data for one or more AI integrations. Though this is only 20% of total customers, they represent about 80% of our ARR. And our customers' usage of AI within that platform continues to grow rapidly. 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 API calls quadrupled quarter-over-quarter and the number of Bits Assistant messages increased significantly in that period. While we are aggressively building with 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 GA. Experiments work hand-in-hand with our feature flagging product and combine best-in-class statistical methods with real-time observability guardrails among alternatives 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, real user monitoring, profiler and database monitoring. APM automatically identifies performance and reliability issues and, most importantly, provides explainability and actionable guidance. We also 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 authorization 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. So our teams were hard at work again, and we're looking forward to sharing many new products and future announcements at our DASH conference on June 9 and 10 in New York City. Now let's move on to sales and marketing and highlight some of the deals we closed this quarter. First, we landed two large deals, one seven-figure and one eight-figure annualized deal 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 protocols make 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-power 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 stand-alone 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. Next, 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 the 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 Security Agent to accelerate incident response with AI. Next, 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 had 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, and will have unified visibility across their cloud and on-prem environment. This customer will expand to 11 Datadog products. Next, we landed a six-figure annualized deal with a Fortune 500 insurance company. This company's fragmented observability stack led to long outages with incidents 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 in LLM Observability. Next, we signed a multiyear expansion with one of the world's largest travel groups in APAC. This customer was using Datadog in 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 customer saved money and improved platform resiliency and performance. This multiyear commitment positions Datadog as their strategic observability provider. And 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 of user activity with cost control they previously lacked. This customer will start with five Datadog products. And 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 Agent, Bits AI Security Agent, 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. But 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 types of users, whether humans or AI agents, so they can transform, innovate and drive value through AI and cloud adoption. And with that, I will turn it over to our CFO, David.

David Obstler, CFO

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 Q1 since 2022. And our $53 million quarter-over-quarter revenue added is the highest ever for 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 doubled versus a year-ago quarter. These included wins in observability and included some of our newer products like security, data observability and Flex Logs. And 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 the 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, co-generation tools and vertical-specific AI solutions. Next, regarding our retention metrics. Our trailing 12-month net revenue retention percentage was in the low 120s, 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 multiyear 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; 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 or 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, 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%. And 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 conservatism on these growth trends. In addition, as with last quarter, we are applying a higher degree of conservatism to our largest customer. So for the second quarter, we expect revenues to be in the range of $1.07 billion to $1.08 billion, which represents 29% to 31% year-over-year growth. This guidance implies sequential revenue growth of $64 million to $74 million or 6% to 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 to $235 million, which implies an operating margin of 21% to 22%. As a reminder, in Q2, we will be holding our DASH user conference which we estimate will 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 to $0.59 per share based on approximately 369 million weighted average diluted shares outstanding. And for fiscal 2026, we expect revenues to be in the range of $4.3 billion to $4.34 billion, which represents 25% to 27% year-over-year growth. Non-GAAP operating income is expected to be in the range of $940 million to $980 million, which implies an operating margin of 22% to 23%. And non-GAAP net income per share is expected to be in the range of $2.36 to $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 to $40 million. We continue to apply a 21% non-GAAP tax rate for 2026 and going forward. And we expect capital expenditures and capitalized software together to be 4% to 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. And I want to thank Datadog's worldwide team for their efforts. With that, we'll open the call for questions. Operator, let's begin the Q&A. Thanks.

Operator, Operator

Our first question today is coming from the line of Mark Murphy of JPMorgan.

Mark Murphy, Analyst, JPMorgan

Congratulations on an amazing performance. Olivier, is there any way to conceptualize the growth in the sheer raw volume of code being produced in the world today due to adoption of code generators such as Copilot, Codex and Cursor, because they seem to be developing the capability to take on full projects and 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.

Olivier Pomel, Co-Founder & CEO

Well, we definitely think and see that there's many more applications being created. There's going to be way more complexity in production. We see some of that happening already today. Some of those new applications are getting into production; they're finding users. We see some signs of that at every layer of our platform. We quoted a few stats on the increasing data volumes. We see AI products; that's definitely a reflection of that. So we see an inflection point 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.

Mark Murphy, Analyst, JPMorgan

Okay. And as just a quick related follow-up. If we click down one layer, I'm wondering how you might view the increasing heterogeneity of the environment at the silicon level, because when you look across the cloud providers with Tranium and Graviton and Google with TPUs, Microsoft has launched their own silicon. It looks like that is starting to explode. Our understanding is that trying to monitor the mixed environment is a lot more difficult than if you just have a uniform fleet of Intel and AMD chips, and we keep hearing all the traditional monitoring tools really fail on the custom silicon and Datadog handles it well. And then all this new telemetry, including high-bandwidth memory and that type of thing. Can you speak to whether that trend is giving you some tailwinds?

Olivier Pomel, Co-Founder & CEO

Yes. I mean, the broader market that's interesting here is training. Training used to be something only two or three companies were doing at large scale. And it looks like training actually might democratize quite a bit more, and many companies will train models on a regular basis. So it becomes more of a viable category for service providers like us. I think the heterogeneity of the silicon is definitely a trend that plays in our favor there. The more heterogeneous, the more you need someone else to make sense of everything for you and tie it together and also tie it with the non-GPU aspects and the rest of the infrastructure, the application, the users, and the developers — basically everything we do for customers. When you think of who actually has heterogeneous environments today, that is still a relatively small number of companies; Google barely just started selling their TPUs to the outside. So I think it's still a small number, but we see a growing opportunity there. Interestingly, last year, when we reported earnings, we said we're mostly interested in inference workloads and training was not a real 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, and monitor the GPUs. So we see that as a point of validation that there's going to be a fit for us.

Mark Murphy, Analyst, JPMorgan

That's amazing to think there's a whole need to mention if you can move from inferencing into the training side. And I caught the reference in the prepared remarks of how you landed a couple of those very large labs. So congrats on everything.

Operator, Operator

And our next question will be coming from the line of Sanjit Singh of Morgan Stanley.

Sanjit Singh, Analyst, Morgan Stanley

I want to start with David on this guide to start the year; it's 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 check on the overall macro backdrop — we do have some geopolitical tensions and those types of things when we think about. Are you seeing any impact in the Middle East-based business or any impact in your e-commerce or retail business where there may be some consumer discretionary impacts? I just want to get how you're thinking about those parts of the business. And then I had a follow-up for Oli.

David Obstler, CFO

Yes. We had a very strong quarter across the board. We have a multi-industry, multi-geography type of quarter, and SMB was very strong. That is the source of our guidance and our confidence — that type of performance. We haven't seen a particular effect in the consumer businesses or e-commerce businesses yet. We basically have a continuation of trends in those businesses; travel and things like that are very similar to the other industries. So we haven't seen it yet. We obviously watch it and look at analytics, but we haven't seen a material impact. In terms of our overall guidance, the trends that we have are incorporated and we discount across the board, and I think we mentioned our particular treatment of our largest customer.

Sanjit Singh, Analyst, Morgan Stanley

That's very clear. And then Olivier, for you. When we talk to investors about the debate in this category longer term, it is: what does the category look like when agents are doing the triaging and investigating versus human engineers and human SREs? What is your vision of how that evolves for Datadog, both from a product standpoint and an experience standpoint from a UI perspective, but also: is there going to be new pricing modalities in terms of pricing when agents are consuming the Datadog platform to a higher degree than engineers do today?

Olivier Pomel, Co-Founder & CEO

Yes. Look, I think one thing I'd say is it's hard to tell where we're going to be in four or five years. If you had told me two years ago that most engineers would go back to coding in the console, I wouldn't have believed you. And yet, that's one of the winning modalities today. As far as we're concerned, we don't care whether most of the usage is humans or agents. Our business model lends itself to do pretty well — we are usage-based so it doesn't really matter where the usage is coming from. The way we see trends right now is we see both a stratospheric increase in agent usage. We have a ton of usage on our MCP Server. We see customers spending to automate a lot with their own agents using our agent capabilities. But we also see an increase in usage of the web interface by humans. Right now, the two work hand-in-hand and we keep developing and pushing on both fronts.

Operator, Operator

Next question is coming from the line of Raimo Lenschow of Barclays.

Raimo Lenschow, Analyst, Barclays

One for Olivier, one for David. Olivier, if I listen to you in your prepared remarks, there's a lot of consolidation where people try to use open-source tooling and then realize they need to come to you. On the other hand, in the industry we still have a lot of noise around that level. How do you see it in real life? To me, it seems optionability is very hot. And then there are different categories where you use certain vendors and some open source. Can you speak to what you see in real life there?

Olivier Pomel, Co-Founder & CEO

I mean, in real life, most companies have open source in some capacity somewhere. When it comes to having a platform that unifies everything, correlates everything and does more of the problem solving for you, that's typically what customers use us for. The motion we see pretty much everywhere: customers have 4, 6, 7, 15, 25 different tools — different pockets in the organization, different business units — and it's a huge mess. They come to us because they can unify all that. They get better results because all of the data is in one place, the workflows can be automated end-to-end, teams can get end-to-end visibility, and they don't have blind spots. Also they save money because they don't have all these pockets of inefficiency. So it's a win 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 and they certainly have the balance sheet and the human capital to support some of that build-out. If there were ever a set of companies for whom it makes sense to do it themselves, it would be them. Yet, we see that they have the same issue: when it comes to going as fast as they can and being as efficient as they can with their resources, they come to us to replace some of the things they were building before.

David Obstler, CFO

Two metrics to look at to make the point Oli is 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 products, that shows that consolidation on the Datadog platform has continued and there's a very strong trend. Part of that is the movement of solutions, as Oli mentioned, from both open source and 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.

Raimo Lenschow, Analyst, Barclays

Okay. Perfect. And then, David, last year you did a lot of investments around go-to-market, especially on sales capacity. If you think about now the non-AI category doing better, how much of that is people and the cloud migrations — that's an industry trend — and how much of that is you guys actually being better positioned?

David Obstler, CFO

It's a number of things, including the expansion of the platform, the consolidation, and the successful ramping of sales capacity, which while not jeopardizing productivity, has resulted in ARR increasing. There's also been a good environment which helped. I think the investments we've made in go-to-market and are continuing to make are paying off and are the right decision.

Olivier Pomel, Co-Founder & CEO

Yes. At the end of the day, there are clearly some market tailwinds with the adoption of AI, but we are outperforming our competitors at scale, and we're taking share. That relates to the structural platform where 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.

Operator, Operator

Our next question is coming from the line of Gabriela Borges of Goldman Sachs.

Gabriela Borges, Analyst, Goldman Sachs

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 I had a follow-up for David — how do we think about the attach rate on training versus inference observability? Is there a way to benchmark observability spend as a percentage of inference spend, and does that number change given the new data you're seeing on the training side as well?

Olivier Pomel, Co-Founder & CEO

So 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. 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 every minute you lose in training is a competitive disadvantage. As a result, it becomes way more interesting as a market for a company like us. We see some signs of that; we didn't have a lot of it last year, and now we're starting to see quite a bit of activity and demand. We have success landing large customers with those products. On the attach question, David?

David Obstler, CFO

Yes. Going back to the metrics that Oli talked about in terms of attach: we said that 6,500 customers are using our AI integrations, which is 20% of customers and about 80% of ARR. So there is attach. I think it's earlier days for the training side — training looks like it will be a contributor — but that's early. I would look at the larger attachment at this point as evidence of inference, but also some training.

Operator, Operator

Our next question is coming from the line of Karl Keirstead of UBS.

Karl Keirstead, Analyst, UBS

Okay, great. I wanted to start with Olivier and David — congratulations on reaching that $1 billion milestone, well done. David, maybe the question is for you and to hone in specifically on the Q2 guide. Even if you put up a modest beat on that guide, it's going to be by order of magnitude the largest sequential dollar increase in the company's history, I think. I just wanted to unpack what's giving you that confidence? In particular, is there anything interesting to call out in terms of the ramp of a couple of the larger research labs, one of which renewed with you guys in Q4 and another one just landed? I presume they're ramping nicely in Q2, but would love any color.

David Obstler, CFO

Yes. Let me unpack this in a couple of ways. As you know, we're a recurring revenue model. So the biggest indicator in the near term for the next quarter is the ARR growth in the previous quarter. When we said we had a record, at the bedrock of this is the run forward of ARR that we've already signed. The ARR add was very broad-based and not very concentrated. We pointed out some significant adds, but the first quarter ARR add was really diversified and from lots of different places. The confidence we have is based on what we've already accumulated in ARR, and we apply our usual conservatism. That produces what you observed, which is a very impressive sequential increase due to what happened in Q1 and the rate of business accumulation by Datadog.

Olivier Pomel, Co-Founder & CEO

If you want to dive into what David just said: the adds are broad-based. When you look at why we had a great Q1, we also had customers we landed in Q4 that contributed in Q1. But even if you take out the customer we landed in Q4 that added the most revenue in Q1, we still had a record quarter in terms of ARR add. So this is really broad-based. We also landed a few more customers in Q1 that don't contribute revenue yet, but we expect them to be big contributors in the future. Put all that together and we feel very confident about Q2, hence the numbers you've seen.

Operator, Operator

And our next question is coming from the line of Fatima Boolani of Citi.

Fatima Boolani, Analyst, Citi

Olivier, I wanted to double back on a question that was asked earlier with respect to telemetry volumes essentially going parabolic, and you are accessing a brand-new demand in the foray into training and monitoring and observing model environments inside some of the world's largest frontier labs. I wanted to ask about the structural changes to the capital intensity of the business. Your CapEx levels are still 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. And then as a related matter, we've seen a rise of sovereign data and data residency requirements ramp as AI models move into the territory of national security. Just wondering if you can talk to some of the engineering horsepower internally that you're leveraging to be able to keep a tight command on capital intensity and gross margins?

Olivier Pomel, Co-Founder & CEO

Yes. The investments we're making right now: we run most of our workloads on cloud, meaning you'll see all of that in OpEx, not CapEx. So we have low CapEx. If that changes, we'll tell you. Right now we are ramping up our investments in particular in R&D and in the scale of the models we train ourselves, but there's nothing in the numbers today that moves any needle on CapEx. If that changes, we'll tell you. We don't expect any change to our CapEx profile. On data residency and sovereignty: we definitely see more demand for that. For us, that means investment in deploying into more geographies and having more certifications to sell to the public sector and to higher security customers. So we mentioned today a data center in the U.K. and our FedRAMP High authorization; we're not stopping there in terms of certifications we're pursuing. Another area of investment is our bring-your-own-cloud and on-prem products where we can run on our customers' infrastructure. We have products in that area and significant investment so we can support customers that want to operate in a separate environment from our SaaS offering.

Operator, Operator

And our next question is coming from the line of Curt of Evercore.

Curt, Analyst, Evercore

Congrats, nice start. Oli, I was wondering if you could give some thoughts on security for agents. I think one of the big issues in terms of getting agents into production is security. How do you see Datadog plugging into that opportunity? And then just a quick one for David: congrats on the FedRAMP milestone. Are your partner relationships in place to take advantage of this? I realize it will be a long-term opportunity, but curious how well established you are to start seeing some bookings in that area.

Olivier Pomel, Co-Founder & CEO

On the security of agents, we approach it in two ways. First, we're building automation ourselves because we are embedding a lot of automation into our products for customers and agents that automatically identify and in some cases resolve issues without a human having to do everything. A lot of this work is about understanding what permissions to apply, what guardrails to implement, how to design the human interface, and how to make the system trustworthy and visible in the right way. That's a large product surface area for us and one of our key investments. You should expect to hear more about that at our conference. Second, on the security aspects more generally: we believe security needs to be integrated and cannot be a point solution that looks at a single sliver of the posture. You need to look at everything together — observability plus security — and that's one of the areas we cover with our security efforts as part of the whole platform.

David Obstler, CFO

On FedRAMP: we've been working on different certifications and 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 in this sector, building pipeline takes time. Channel partner relationships are an important part of this, and we've been investing there, though we will continue to invest more.

Operator, Operator

Our next question is coming from the line of Patrick Colville of Scotiabank.

Patrick Edwin Colville, Analyst, Scotiabank

I guess, Olivier and David, you guys are very deliberate in your messaging in the prepared remarks. I want to double-check one comment. David, you said you applied a higher degree of conservatism to the largest customer. Did I hear that right? And 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?

David Obstler, CFO

It's both. It's the same guidance methodology we've used, and we're being explicit. For all the business except for the largest customer, we've always taken the drivers and applied discounts. For this particular customer, we took a higher degree of conservatism than for the rest of the customer base and discounted it more. We were explicit in the remarks and you interpreted it correctly.

Olivier Pomel, Co-Founder & CEO

I wouldn't put too much extra weight on the specific wording — we are deliberate, but the methodology hasn't changed. Both David and I have slightly raspy voices today, but the methodology is consistent.

Operator, Operator

Our next question is coming from the line of Peter Weed of Bernstein Research.

Peter Weed, Analyst, Bernstein

I'll echo others on the momentum — great to see. One of the great successes you talked about was landing a couple of the AI labs for the hyperscalers. Although you've talked in the past that hyperscalers typically build observability in-house, what is it about the AI workloads that are making it more attractive for them to use Datadog? And what might give you confidence that Datadog might be more persistent with them in these types of workloads — a signal for how other customers might use Datadog around AI that is differentiated from things they might bring in-house?

Olivier Pomel, Co-Founder & CEO

For all customers, AI workloads are high stakes, high complexity, and not core to their differentiation in many cases. They require reliability and speed. What we built our business on — unifying telemetry, automating workflows, and removing blindness across the stack — is very relevant here. Hyperscalers have often built tooling themselves because they have large engineering teams and unique scale, but the urgency of their development efforts now forces a focus on what's core versus not core. That urgency, coupled with the complexity of training workloads and the need for specialized monitoring like GPU monitoring, makes third-party solutions like Datadog compelling. It's still early, but we see signs this could be a bellwether for how other companies adopting training workloads will want to instrument their stacks.

Operator, Operator

The line of Gregg Moskowitz of Mizuho.

Gregg Moskowitz, Analyst, Mizuho

I'll add my congratulations on a terrific quarter. One for me: Olivier, I know the Cloud Prem offering is not GA yet, but curious if you have any early feedback on your new on-prem/cloud-prem offering that provides the ability for Datadog to run on customer infrastructure. Could this be another incremental growth opportunity for Datadog? What are your expectations for this?

Olivier Pomel, Co-Founder & CEO

We definitely see a great opportunity there. It addresses customers who need data residency or want to run monitoring in their own environment. Today, that is not the largest part of the market, but we see potential for it to be a meaningful growth lever over time. We're investing heavily in that product and seeing interesting customer traction. It can also help us compete for extremely large-scale workloads where customers would not have considered a SaaS offering before, so it's exciting.

Operator, Operator

That was our last question. I will now turn the call back over to Olivier Pomel for closing remarks.

Olivier Pomel, Co-Founder & CEO

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

Operator, Operator

This concludes today's program. You may all disconnect.