Datadog Inc ($DDOG) Q1 2026 Earnings Call

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 continue to grow and diversify rapidly both in the number of customers we serve and the scale of those customers. And 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 a 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 $1.01 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 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 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'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. But we're 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 early life cycles. We believe each has a 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 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 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. 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 and our customers' usage of AI within Datadog platform continues to grow rapidly. Bits AI security 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 our data 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, real user monitoring, profiler and database monitoring, APM Recommendations automatically identify performance and reliability issues and most importantly explain how to fix them. And we announced our plans to launch our next data center in the UK. 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 US 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 US and internationally. So, our teams were hard at work again and we're looking forward to sharing many new products and feature announcements at our DASH conference on June 9th and 10th 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, a seven-figure and an 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. But 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 and 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 Incident Management. 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 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 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 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 and will have unified visibility across their cloud and on-prem environments. 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 incident support 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 customer will save money and improve platform resiliency and performance. This multi-year 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 over user activity with a cost control they also 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 Agent, Bits AI security analyst, Bits Assistant, Datadog Incident Management, 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.

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. 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. So 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.

Yeah, I mean look, the broader market that's interesting here is if training, you know, training used to be something only two or three companies were doing or maybe four or five at a 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 a 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 with the non-GPU aspects and the rest of the infrastructure and the applications, the users and the developers, like basically everything we do for a living. 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, so I think it's still a small number of companies out there 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. So we see that as a point of validation that there's going to be a great market for us.

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. You know, 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, 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, then it doesn't really matter where the usage is coming from from that perspective. The way we see trends come up right now is we see both a stratospheric increase of agent usage. So 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. But we also see an increase of usage of the web interfaces by humans. So right now the two work hand in hand, and we keep developing and pushing on both fronts.

I mean, in real life, you know, most companies have open-source in some capacity somewhere. But you know, when it comes to having a platform that unifies everything, does more of the problem-solving for you, that's typically what customers use us for. And the motion we see pretty much everywhere is customers have four, six, seven, fifteen, twenty-five different things in different pockets in the organizations and different business units, and it's a huge mess. And 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, and also they save money because they don't have all these pockets and inefficiency everywhere. 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. And hyperscalers typically have a culture of building everything themselves, and you know, they suddenly have the balance sheet and the human capital to support 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. And yet, you know, we see that they have the same issues 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 metrics to look at to make the points Ali's making: if you look at our platform adoption, you see both the growth of the different categories and the extension of the categories out to lots of products. That shows you that the consolidation on the Datadog platform has continued and is a very strong trend. And part of that is the movement of solutions, as Ali 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.

Yeah, and look, at the end of the day, there's clearly some market tailwinds with the adoption of AI. But also we are outperforming all of our competitors at scale and we're taking share. And that relates to the structure of our 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.

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, like it was not a production workload. It was something that researchers were building and that was very one-off and homegrown in ways. And 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 failure you have in your training run is a week you give away to the competition. And so as a result, you know, it becomes way more interesting as a market for observability. And 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. Then we have success landing with large customers with those products.

Yeah, I would, I mean, I basically want to double down on what David just said. The adds were broad-based. I mean, look, and when you look why do we have a great Q1, we also landed great customers in Q4. We had talked about it a quarter ago. 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 adds. So this is really broad-based. And 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. So when you put all that together, we feel very confident about Q2, hence the numbers you've seen.

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, so we have low capex. Now, 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 upfront, 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. So that's on the capex side. We're very different businesses in that way from the AI lab. On the subject of data residency and sovereignty of AI, you know, things like that, we definitely see more push for that, more demand for that in the customer base. And for us, that means investments in two areas. One is in deploying it into more geographies and having more certifications to sell to the public sector and to the highest levels of the public sector. You know, so we mentioned today data center in the UK, for example, and the FedRAMP High certification. We're not stopping there in terms of the certification we're going after with the federal government. So 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. And so 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.

Yeah. So, on the security of agents, we interface with that in two ways. So 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. And 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. And so that's pretty much the whole product surface is figuring that out. The automation itself actually kind of works already. So you should expect to hear more about that at our conference. This is definitely one big area of investment for us on the security aspects of running agents. Look, 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 get everything all together, and that's one of the areas that we are also covering with our security efforts. So that's part of the whole platform strategy.

We, I would say though, I wouldn't give that much weight to the very specific way that we're deliberate, but not all that deliberate. You know, and similarly both David and I have a raspy voice today but there's no meaning. But I will remind everybody that we did not change. So 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 that we've had. So no change, but this has been what we've always been doing.

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. And we see the hyperscalers that are coming to us for training workloads in particular being very interested in that. So again, it's too early in the product life cycle and in the customer life cycle for these specific customers to call definitive victory there. But 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 the next 10, 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.

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 what's differentiated. They can't afford to be late. And it's a really hard job to do all of that. So that's what we build our whole business on. And it's also very true at the highest level for the largest companies.

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

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