Datadog

“We incorporate AI, including generative AI, into our products. These technologies are complex and rapidly evolving and building them requires significant investment in infrastructure and personnel with no assurance that we will realize the desired or anticipated benefits.”

“In addition, our business may be disrupted if any of the third-party AI services we use become unavailable due to extended outages or commercially unreasonable terms of service.”

“This increase was primarily due to an increase of $158.9 million in personnel costs including allocated overhead costs for our engineering, product and design teams as a result of increased headcount and an increase of $30.4 million in cloud infrastructure-related investments.”

“Third, next-gen AI introduces new complexity and observability challenges. We are addressing this with what we call Datadog for AI to observe and secure the AI stack from end to end. This includes GPU Monitoring, Agent Observability, Agent Console, Data Observability, AI Guard, and many other products. Finally, our AI research team and our large volume of rich data using critical workflows enable us to conduct groundbreaking research.”

“Now beyond Toto, we are working on larger and more ambitious dedicated models, post-training models to power Bits AI and bringing other modalities beyond time series data into world models that we think can lead to a step change in capabilities for our customers. We plan to accelerate these research efforts with the acquisitions of Adaptive ML, which will close in June.”

“For a gross margin of 79.6%. This compares to a gross margin of 80.2% last quarter, 80.9% 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. There's no change in our expectations for gross margin, which has been in the 80% ± range historically.”

“Acquired Adaptive ML, a frontier AI startup developing the world’s first Reinforcement Learning Operations platform, to accelerate Datadog AI Research’s investment in world models and agentic LLM post-training for observability—combining Datadog’s access to real-world infrastructure and security data with Adaptive ML’s expertise in building specialized, high-performance AI agents.”

“Cost of revenue increased by $74.1 million, or 45%, for the three months ended June 30, 2026 compared to the three months ended June 30, 2025. This increase was primarily due to an increase of $64.1 million in third-party cloud infrastructure hosting and software costs and an increase of $5.2 million in personnel costs including allocated overhead costs as a result of increased headcount.”

“Our gross margin decreased for the three months ended June 30, 2026 compared to the three months ended June 30, 2025, primarily as a result of increased spend with our third-party cloud infrastructure provider costs.”

“Research and development expense increased by $90.5 million, or 23%, for the three months ended June 30, 2026 compared to the three months ended June 30, 2025. This increase was primarily due to an increase of $62.5 million in personnel costs including allocated overhead costs for our engineering, product and design teams as a result of increased headcount and an increase of $28.2 million in cloud infrastructure and software-related investments.”

“We incorporate AI, including generative AI, into our products. These technologies are complex and rapidly evolving and building them requires significant investment in infrastructure and personnel with no assurance that we will realize the desired or anticipated benefits.”

“In addition, our business may be disrupted if any of the third-party AI services we use become unavailable due to extended outages or commercially unreasonable terms of service.”

“This increase was primarily due to an increase of $158.9 million in personnel costs including allocated overhead costs for our engineering, product and design teams as a result of increased headcount and an increase of $30.4 million in cloud infrastructure-related investments.”

“Research and development expense consists primarily of personnel costs for our engineering, service and design teams.”

“For example, in prior periods customers in our cloud-native cohort, and more recently larger customers in our AI-native cohort, which cohort represented approximately five percentage points of our year-over-year revenue growth for the quarter ended December 31, 2024, have rapidly increased their usage of our product and then optimized or may in the future optimize their usage.”

“As a data point, ARR for more Next-gen AI customers was about 3.5% of our total, a strong sign of the growing ecosystem of companies in this area.”

“The customers we have that are the most scaled on AI workloads are the model providers themselves, and they tend to have their own infrastructure for monitoring the quality of the models.”

“Our revenue growth in Q2 has accelerated across our customer base. On one hand, our AI native customer cohort continued to grow and diversify, both in the number of customers we serve and the scale of those customers.”

“Next, we landed seven-figure annualized deals with two neuro labs. These AI labs are rapidly scaling their AI model training workloads and preparing for major product launches. By deploying observability using Datadog, they gain visibility across their training infrastructure and GPU fleets and can iterate faster on their AI models. They are also using Bits AI to rapidly build monitors, dashboards, and alerts for deep observability context.”

“Finally, we signed a nine-figure renewal with a leading AI company. This longtime, very large customer uses 17 Datadog products to enable unified visibility on production workloads at a very large scale, albeit with a user reduction starting in Q3, which we considered in our guidance and which David will speak to.”

“First, AI is a tailwind for Datadog today as cloud consumption grows and drives more use of our platform. As of Q2, over 750 AI customers use Datadog to monitor and improve their tech stacks. When we look at the largest companies driving AI, all 10 of the top 10 AI leaders are Datadog customers. Beyond AI natives, we see AI activity growing across our broader customer base.”

“Revenue growth accelerated with our broad base of customers, excluding AI customers, to the high 20s year-over-year, up from the mid 20s% last quarter and 18% 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 customers continued to grow rapidly and diversify in the quarter.”

“This 750-strong customer group includes a broad range of AI startups as it has in the past, but now also includes hyperscalers using Datadog for in-house AI labs. In Q2, this includes 31 customers spending more than $1 million annually, of which eight customers spent more than $10 million annually.”

“The portion of our year-over-year revenue growth that relates to new customers was about 30% in Q2, up from 25% in Q1. Geographically, we're performing well in all regions with growth acceleration across the regions. We see particular strength in the Americas as much of the AI activity is occurring in the U.S.”

“Regarding our largest customer, we have seen a usage reduction which is incorporated in our Q3 and full year 2026 guidance. As Olivier noted, this customer has recently renewed with us.”

“Yeah. The last thing I will say, because I know also it's on people's minds, is if you backed out our largest customer from our growth, you get pretty much the same growth rate as the rest of the business has been accelerating very steadily.”

“These are companies that didn't exist a few years ago. What's interesting about them on the use case there is that very often we land customers when they go into production, they release products, and they start serving their customers. In this case, these are customers we're getting as they are training models, they're using us to observe and improve and optimize the training of the models.”

“Well, I can't speculate, but what I will say is, look, the business overall is growing at the same rate if you exclude that customer, as I said. The business has been accelerating overall.”

“Yeah, I think we commented in the remarks that the non-AI has accelerated, the AI, excluding the largest customer, continues. I think we gave those trends in describing the business.”

“For example, our AI-native cohort, which cohort includes our largest customer, contributed high single digit percentage points to the total Company year-over-year revenue growth for the quarter ended June 30, 2026. This cohort rapidly increased their usage of our product, and may in the future optimize their usage, or may fail to renew their subscriptions. Beginning in the third quarter of 2026, we saw our largest customer reduce their usage.”

“Revenue increased by $294.7 million, or 36%, for the three months ended June 30, 2026 compared to the three months ended June 30, 2025. Approximately 70% of the increase in revenue was attributable to growth from existing customers, and the remaining 30% was attributable to growth from new customers. We saw a reduction in usage from our largest customer starting in the third quarter of 2026, which may cause a deceleration in revenue growth.”

“And if I go back to the example of hyperscalers, we actually have teams at the hyperscalers that use us for application and infrastructure or logs internally, even though they've built a lot of that tooling themselves.”

“I will say also on AI adoption that some of the revenue jumps you might see from the cloud providers might relate to supply of GPUs coming online and a lot of training clusters being provisioned. Those typically won't generate a lot of new usage for us.”

“This 750-strong customer group includes a broad range of AI startups as it has in the past, but now also includes hyperscalers using Datadog for in-house AI labs. In Q2, this includes 31 customers spending more than $1 million annually, of which eight customers spent more than $10 million annually.”

“In addition to that, we've mentioned in previous calls, we've also landed the AI Lab or super intelligence labs of a number of hyperscalers. I would say the workloads are similar in that it's largely training of the models, the customers are a bit different. These are very large companies that in that case, previously had a lot of homegrown technology to observe and run workloads.”

“LLM, or Large Language Model, Observability provides end-to-end tracing of LLM chains with visibility into input-output, errors, token usage, and latency at each step.”

“So we have products for monitoring, not just the infrastructure but what the LLMs are doing. Those products are still not in GA. We're working with a smaller number of design partners for that.”

“To help customers understand AI technologies and bring them into production applications, our AI integrations allow customers to pull their AI data into the Datadog platform. Today, about 2,000 of our customers are using one or more of these AI integrations.”

“Third, we expanded Datadog for AI, our products that observe, secure, and optimize the AI stack from end to end. Data Observability enables companies to trust the data being used by AI with lineage quality monitoring and jobs monitoring. Bits Data Analysis uses a rich data context to accurately answer business questions. Agent Console provides visibility into AI agent usage, cost, and effectiveness.”

“Next, we landed seven-figure annualized deals with two neuro labs. These AI labs are rapidly scaling their AI model training workloads and preparing for major product launches. By deploying observability using Datadog, they gain visibility across their training infrastructure and GPU fleets and can iterate faster on their AI models. They are also using Bits AI to rapidly build monitors, dashboards, and alerts for deep observability context.”

“Third, next-gen AI introduces new complexity and observability challenges. We are addressing this with what we call Datadog for AI to observe and secure the AI stack from end to end. This includes GPU Monitoring, Agent Observability, Agent Console, Data Observability, AI Guard, and many other products. Finally, our AI research team and our large volume of rich data using critical workflows enable us to conduct groundbreaking research.”

“The second thing we see is a very rapid increase in the usage of all of our AI-first surfaces. That would be the products that measure agents and LLMs, where we see an explosion of traffic in terms of the LLM and tool calls we're getting. That would be the amount of calls we're getting to our MCP endpoints. We see that explode completely over the past two quarters.”

“We mentioned our GPU Monitoring product is actually getting quite a bit of usage in a number of neuro labs and very AI-first types of customers. We're also seeing an explosion of volume in our agent monitoring product, we're well-positioned there.”

“I would say three to six months ago. The focus has moved quite a bit towards cost. Customers were spending a lot on AI, and they were wondering how to optimize cost.”

“Just want to add that when you look at what we describe as some of our deals in the quarter, and you look down our description, you'll see that a number of them have the AI products included. That is indication that those large enterprises are using the platform and buying the AI products as well.”

“What we do for our customers today, especially as they keep adopting AI, is we help them save a lot of the money they would spend on building, running operations or running AI agents. When we have a concern with customers, that's the one thing they kept mentioning is, "Hey, how can you help me rein in my AI costs?”

“We think also that the multiplication of models, and open source models in particular, opens the door to customers doing a lot more training on their own. That's a new market for us. We see some signs that we have a very good role to play there.”

“We've seen an explosion, basically, of the volume we're getting there. We get more usage from different kinds of companies, so we definitely see that. We see it also across traditional companies and some more recent AI natives. We see a little bit of both. I would say for that category, it's still super early. We expect the products to change quite a bit. We expect the usage, maybe also the packaging to change over time quite a bit.”

“Launched more than 100 new capabilities at DASH 2026 to help customers drive autonomy and manage growing AI and security complexity. Highlights included fully autonomous Bits AI for end-to-end incident detection, investigation, and remediation; AI Guard to protect AI agents from prompt injection and poisoning attacks; Bring Your Own Cloud for deploying Datadog within customer environments; and Bits Agent Builder for creating custom AI agents.”

“Our customers are building and deploying with AI, and they are using the Datadog platform to observe, secure, and act on their AI-enabled solutions.”

“Finally, we launched a number of innovations to secure the AI stack and defend against a new class of AI-powered attacks. AI Guard agent discovery finds and maps every known and unknown custom agent so security teams can see what is protected and what is not.”

“There's opportunity at every layer of the stack in inference. We do think at the end of the day, inference will be the dominant workload. That anytime you train, you probably will want to infer more than you train, as a rule of thumb.”

“Our platform's Watchdog capabilities feature artificial intelligence and machine learning that can cross-correlate metrics, traces, logs, sessions, security signals, and other data to identify outliers and notify users of potential anomalies; discover and help resolve issues quickly with automated root cause analysis; augment the troubleshooting workflow with contextual insights; and minimize impact on customers.”

“By combining Event Management with Watchdog, Bits AI, and workflow automations, Datadog now provides a full AIOps solution that helps teams automate remediation, proactively prevent outages, and reduce the impact of incidents.”

“In the next-gen AI space, we announced general availability of Bits AI for incident management. By using Bits AI for incident management, incident responders get auto-generated incident summaries to quickly understand the context and scope of a complex incident.”

“First, we expanded Bits AI to accelerate and automate the DevOps loop. This is the loop that goes from detection to investigation to remediation that engineers go through each time something breaks.”

“Next, we landed seven-figure annualized deals with two neuro labs. These AI labs are rapidly scaling their AI model training workloads and preparing for major product launches. By deploying observability using Datadog, they gain visibility across their training infrastructure and GPU fleets and can iterate faster on their AI models. They are also using Bits AI to rapidly build monitors, dashboards, and alerts for deep observability context.”

“Bits AI investigation is already speeding up incident resolution and reducing expensive escalations. This customer will expand to 19 Datadog products.”

“We are also seeing signs of rapid growth in agentic activity with a number of MCP tool calls quadrupling again quarter-over-quarter and growing more than 22x when compared to Q4 2025. Second, we are delivering AI for Datadog to deliver more value and greater platform capability to our customers. This includes our Bits AI products, chat, investigation, detection, Bits Code, Bits Testing, Bits Release, and many others.”

“The second thing we see is a very rapid increase in the usage of all of our AI-first surfaces. That would be the products that measure agents and LLMs, where we see an explosion of traffic in terms of the LLM and tool calls we're getting. That would be the amount of calls we're getting to our MCP endpoints. We see that explode completely over the past two quarters.”

“When they use Bits AI, they use more of our product. They deploy more of it. They create more dashboards and alerts and everything else. They have more users inside of our product. It's not a zero sum game.”

“We also are changing the way we package it. We have a new model with AI credits that we're rolling out just because the surface of contact is so much wider now than the specific feature. There's quite a bit that is going on there. The explosion of activity that I mentioned earlier about other parts of our other AI surfaces is happening also in Bits AI.”

“Now we've actually separated the agent from our SIEM so customers can use it with other SIEMs. We do that because the agent performs just so well, and it's been such a differentiator when we pitch the SIEM that we think we're limiting our sales market-wise if we just go after customers that want to re-platform their SIEM, and it can have a much broader appeal as an AI SOC. We are definitely taking moves towards that.”

“Launched more than 100 new capabilities at DASH 2026 to help customers drive autonomy and manage growing AI and security complexity. Highlights included fully autonomous Bits AI for end-to-end incident detection, investigation, and remediation; AI Guard to protect AI agents from prompt injection and poisoning attacks; Bring Your Own Cloud for deploying Datadog within customer environments; and Bits Agent Builder for creating custom AI agents.”

“Launched AI-powered Bits Code, Bits Chat, and Bits Agent Builder for general availability”

“We incorporate AI, including generative AI, into our products. These technologies are complex and rapidly evolving and building them requires significant investment in infrastructure and personnel with no assurance that we will realize the desired or anticipated benefits.”

“You can have Bits AI manage your monitoring and manage your detection for you. You can have it code for you. You can have it generate managed tests. There's all sorts of different use cases that we built into it that broaden the surface of contact, and we see a lot of adoption across all of those different areas.”

“Meanwhile, we are seeing continued experimentation with new technologies, including a growing adoption of AI, which we believe will be an accelerator of technical innovation and cloud migration over time.”

“We tend to be more correlated with the live applications, production applications, and inference workloads that tend to follow after that and that are more tied to all of these applications going into production.”

“On the other hand, and as a great illustration of the breadth of trends across our business, revenue growth for our non-AI customers also accelerated again this quarter to the high 20s% year-over-year, up from the mid-20s last quarter and 18% year-ago quarter. Overall, we continue to see healthy trends in customer demand. Our broad base of customers, from the most nimble startups to the largest and most established enterprises, are all adopting AI.”

“We think this is accelerating their usage of cloud and modern technologies, as well as their usage of the Datadog platform to observe, secure, and act on their cloud and AI workloads.”

“For custom metrics data, we introduced Infinite Cardinality Metrics, which allow our users to answer arbitrarily complex questions as they generate larger amounts of data with AI agents without incurring any extra costs.”

“First, AI is a tailwind for Datadog today as cloud consumption grows and drives more use of our platform. As of Q2, over 750 AI customers use Datadog to monitor and improve their tech stacks. When we look at the largest companies driving AI, all 10 of the top 10 AI leaders are Datadog customers. Beyond AI natives, we see AI activity growing across our broader customer base.”

“Revenue growth accelerated with our broad base of customers, excluding AI customers, to the high 20s year-over-year, up from the mid 20s% last quarter and 18% 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 customers continued to grow rapidly and diversify in the quarter.”

“The portion of our year-over-year revenue growth that relates to new customers was about 30% in Q2, up from 25% in Q1. Geographically, we're performing well in all regions with growth acceleration across the regions. We see particular strength in the Americas as much of the AI activity is occurring in the U.S.”

“We do see broad adoption, and we see it in two ways. One is we see it manifest itself in just more transformation, more cloud adoption, more workloads, more modernization from customers. That's what drives the majority of the known AI customer acceleration.”

“It's largely driven by the existing customers, and it's driven by both increases in volume, and because they're moving more workloads to the cloud, and adoption of our newer products as they consolidate onto us.”

“We've successfully expanded quota capacity, the geography of it, and essentially, that's, as we talked about last quarter, providing returns. That's also being a growth driver in our non-AI or enterprise type business.”

“I think a lot of it has more to do with the fact that the AI agents are largely spending a good amount of their time, like sometimes the majority of their time, coding tools. Tools are just applications that already existed, and those applications typically run on CPUs, so we see quite a bit of that.”

“Our customers are building and deploying with AI, and they are using the Datadog platform to observe, secure, and act on their AI-enabled solutions.”

“Revenue increased by $294.7 million, or 36%, for the three months ended June 30, 2026 compared to the three months ended June 30, 2025. Approximately 70% of the increase in revenue was attributable to growth from existing customers, and the remaining 30% was attributable to growth from new customers. We saw a reduction in usage from our largest customer starting in the third quarter of 2026, which may cause a deceleration in revenue growth.”

“Our sales team is segmented into four revenue-generating areas: an enterprise sales team that sells to large businesses; a high velocity inside sales team that is focused on acquiring new customers; a customer success team that handles new customer on-boarding and expansions in existing customers; and a partner team that works with resellers, system integrators, referral partners and managed service providers.”