DDOGDatadog, Inc.

“we landed 2 large deals, a 7 figure and an 8-figure annualized deals with the AI research divisions at 2 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 selling velocity. But fragmented internal and protocol that it's harder to identify and solve issues and reduce engineering and research productivity. By using Datadog, both companies are accelerating their past of innovation on their hyperscale AI training workload. And this includes optimizing their workflows using GPU monitoring on large power GPU grades.”

“we signed a 7-figure annualized expansion for an 8-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 and CP server.”

“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, cogen tools and vertical-specific AI solutions.”

“non-AI customer revenue growth accelerated again this quarter to mid-20% 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 have all kinds accelerating their use of AI.”

“We now have over 6,500 customers sending data for 1 or more of AI integrations. Though this is only 20% of total customers, they represent about 80% of our ARR.”

“going back to the metrics that Oli talked about in terms of attach, we said that 6,500 customers are using our integrations and 20% of the customers and 80% of the ARR. So there is attach. I think it's earlier days for the training. That looks like it will be a contributor. But 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.”

“SRE agent investigations have more than doubled from December to March. The number of spans sent to our LLM Obeservability product nearly tripled quarter-over-quarter. The number of Datadog MCP server to calls, quadrupled quarter-over-quarter and the number of beef assistant messages increased by a factor of 1 in that period.”

“We delivered this AI security agent, which autonomously triages Datadog Cloud SIEM signals, conduct 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 definitely think and see that the 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 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 run most of our workloads on cloud, meaning you'll see all of that in OpEx, nothing CapEx. So we have low CapEx. 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 front some 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.”

“last year, when we reported earnings, we said we're mostly interested in inference workloads and training is not a real market for us yet. Now we actually see training becoming a market. We started lending 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 landed a 6-figure annualized deal with a Fortune 500 insurance company. This company's fragmented Obeservability stack led to long outages with incident supported first by their customers instead of their tooling. By using Datadog and consolidating 3 legacy APM tools, they expect to move from reactive responses to proactive incident detection. They will adopt 10 Datadog products to start, including all 3 pillars in LLM adorability.”

“When you look in general at large AI customers, they use Datadog at the way other companies are largely with a fairly broad set of our products to cover the full circuit of liability. What's new is we now have a product for GPU monitor. It's a very new product. And we see the hyperscaters that are coming to us for training workloads in particular being very interested in that.”

“we see both stratospheric increase of agent usage. So we have a ton of usage on our MCP server. We see customers spending to automate a lot with their own agency using our agent combination of those. But we also see an increase of usage of the web interface is by humans. So right now, the 2 work hand-in-hand and we keep developing and pushing on those fronts.”

“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 [indiscernible] to accelerate incident response with AI.”

“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. In this quarter, including new land deals with 2 of the world's biggest AI research teams, helping them improve and optimize their training workflows.”

“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.”

“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.”

“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 ongoing 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 every minute you lose is or whether every fellow you have in your training around is a week you give away to the competition.”

“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 analyst Bits Assistant, Datadog IT 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”

“there's the agents will build ourselves because we are building a lot of automation inside of our products 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, what kind of put to interface with the humans and how to make that the trust worthy and visible in the right way.”

“training used to be something only 2 or 3 companies were doing or maybe 4, 5 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 service providers -- selling provider like us basically. I think the heterogeneity of the silicon is definitely a trend that plays in our favor there.”

“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.”

“Our engineers enabled with the latest AI coding tools are building rapidly to help our customers confidently and securely deploy their applications.”

“We also shipped Bit Assistant now in Preview, which helps customers search and act across Datadog using natural language”