Datadog (DDOG) Q3 2026 Earnings Call Transcript

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DATE

Wednesday, Nov. 4, 2026

CALL PARTICIPANTS

• Cofounder and Chief Executive Officer - Olivier Pomel
• Chief Financial Officer - David Obstler
• Senior Vice President, Strategic Finance and Investor Relations - AJ Ljubich

TAKEAWAYS

• Revenue -- $1.20 billion, representing a 35% increase compared to the prior year driven by multi-product platform adoption.
• $100,000+ ARR Customers -- 4,860 accounts, increasing from 4,060 in the previous year and contributing 91% of total ARR.
• Infrastructure Monitoring ARR -- Over $2 billion, growing in the mid-30s% as customers utilize visibility across cloud, on-prem, and GPU environments.
• Multiproduct Adoption (4+) -- 61% of total customers, up from 54% in the prior year.
• Multiproduct Adoption (10+) -- 16% of the customer base, doubling from 8% in the previous year.
• Non-AI Customer Revenue -- Growth in the high-20s%, accelerating for the fifth consecutive quarter.
• Trailing 12-month NRR -- In the low 120s%, with gross revenue retention stable in the mid- to high-90s% range.
• New Customer Revenue Mix -- 30% of year-over-year revenue growth, up from 25% in the third quarter of 2025.
• Non-GAAP Gross Margin -- 81.2%, reflecting platform penetration and engineering optimization projects.
• Non-GAAP Operating Income -- $334 million, yielding an operating margin of 28% compared to 23% in the prior quarter.
• Free Cash Flow -- $251 million, representing a 21% margin for the quarter and a 26% trailing 12-month margin.
• Total Liquidity -- $5.2 billion in cash, cash equivalents, and marketable securities as of Sept. 30, 2026.
• Q4 Revenue Guidance -- $1.19 billion to $1.20 billion, implying growth of 25% to 26% year over year.
• FY2026 Revenue Outlook -- $4.515 billion to $4.525 billion, which management expects to result in approximately 32% annual growth.
• GPU Device Monitoring -- Increased eightfold so far this year as customers scale AI workloads in production.
• AI Cost Tracking Adoption -- Grew over 11-fold, with more than one-third of Cloud Cost Management customers now tracking AI-related expenses.
• AI Customer Count -- Exceeded 850 organizations, up from approximately 750 in the previous quarter.
• FY2026 Non-GAAP EPS -- Projected at $2.73 to $2.75, based on 375 million weighted average diluted shares.
• Capital Expenditures -- Target range of 3% to 4% of total revenue for fiscal year 2026.
• Remaining Performance Obligations (RPO) -- $3.85 billion, an increase of 38% year over year as average contract duration increased slightly.
• Total Customer Base -- 33,500 accounts, up from approximately 32,000 in the prior year.
• Billings -- $1.16 billion, representing 30% growth year over year.
• Japan Financial Services Landing -- Seven-figure annualized deals signed with two of Japan's largest banks, representing platform-wide adoption for cloud modernization.

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RISKS

• Obstler noted that the company's largest customer reduced its usage during the third quarter, though consumption remained above the minimum contractual commitment.
• Obstler stated, "the sequential between Q3 and Q4, tends to be less because of, the late December effect," referring to seasonal usage patterns and year-end code freezes.
• Pomel noted that several early renewals with large customers, while positive for long-term growth, "do create, like, headwind into the short term into the revenue."

SUMMARY

Datadog, Inc. (NASDAQ:DDOG) reported expansion across its infrastructure and security platform as organizations scaled artificial intelligence workloads. Management reported that approximately one-third of the total customer base now uses at least one AI-related product, including three-quarters of customers with over $100,000 in annual recurring revenue. The company stated that its non-AI customer segment growth accelerated for the fifth consecutive quarter while AI-native adoption diversified. David Obstler indicated that revenue guidance reflects conservative assumptions regarding the largest customer's usage and historical year-end seasonality. Olivier Pomel noted that research efforts in reinforcement learning are designed to drive autonomous operations over time.

• Pomel stated, "Scout was built using our in house distributed learning infrastructure, and now powers Bits' investigations."
• The company reported landing its first major deal with a U.S. Federal Defense Agency following its FedRAMP High certification earlier in the year.
• Management indicated that two of Japan's three largest banks adopted the full platform for unified visibility across cloud and on-prem environments.
• The company signed an eight-figure annualized expansion with the AI division of one of the world's largest technology companies, nearly tripling that customer's commitment.
• The company expanded its Datadog for Startups program to provide engineering collaboration for AI-native companies seeing explosive growth.
• Pomel stated, "All 15 of the top 15 AI leaders are our customers," as the company scales its footprint across AI training workloads.

INDUSTRY GLOSSARY

• ARR: Annualized revenue run-rate from subscription agreements.
• Bits AI: A generative AI assistant integrated into the Datadog platform.
• Scout: A specialized reasoning model optimized for site reliability engineering tasks.
• GPU Monitoring: Visibility tools for graphics processing unit health and utilization.
• CCM: Cloud Cost Management, a tool for tracking and governing cloud and AI spend.
• FedRAMP: A government program providing a standardized approach to security for cloud products.
• IL 5: Impact Level 5, the highest level of accreditation for unclassified federal data.
• Agent Observability: Tools for monitoring performance, latency, and costs of AI agents.
• FlexLogs: A logging solution that allows for high-volume data correlation at lower costs.
• MCP Server: Model Context Protocol server enabling standardized communication between AI agents and tools.

Full Conference Call Transcript

Operator: Good day, and thank you for standing by. Welcome to the Third Quarter 26 Datadog Earnings Conference Call. At this time, all participants are in a listen-only mode. After the speakers' presentation, we will open up for questions. To ask a question during the session, you will need to press 1-1 on your telephone. You then hear an unaudited message advising your hand is raised. To withdraw your question, please press 1-1 again. Please be advised that today's call is being recorded. I will now hand it over to our first speaker, AJ Lubich, senior vice president, strategic finance and investor relations. Please go ahead.

Yuka Broderick: Thank you, Victor. Good morning, and thank you for joining us to review Datadog's third quarter 26 financial results. Which we announced in our press release issued this morning, Joining me on the call today are Olivier Pommel, Datadog's cofounder and CEO and David Obstler, Datadog's CFO. First, I would like to thank Yuka Broderick, for excellent work leading our IR function over the last 5.5 years. As she takes on a new role within Datadog. I am excited to step back into the IR function after having worked with many of you through the company's 2019 IPO.

During this call, we will make forward looking statements including statements related to our future financial performance, our outlook for the fourth quarter and for the fiscal year 2026, and related notes and assumptions our product capabilities, 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 or similar indications of future expectations. These statements reflect our views as of 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 Q for the quarter ended 06/30/2026. Additional information will be made available in our upcoming Form 10-Q for the fiscal quarter ended 09/30/2026 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 the most directly comparable GAAP financial measures in our earnings release. and are available at investors.datadoghq.com. With that, I would like to turn the call over to Olivier.

Olivier Pomel: Thanks, AJ. Thank you all for joining us today to go over our Q3 results. Let me begin with this quarter's business drivers. I am pleased to say that we executed a strong Q3. We saw strength across our business, including across our product lines and across our diverse customer base. Revenue growth from our non AI customers accelerated for the 5th straight quarter was in the high-20s percent year over year. Up about 1 percentage point from last quarter up from about 20 percent in the year ago quarter. Additionally, our AI customer cohort remains a strong growth driver. This cohort continue to grow and diversify. In the number of customers we serve and the scale of those customers.

Overall, we continue to see healthy demand. As customers from the most tech forward startups to the largest enterprises in the world are building with AI and with modern technologies. Which we believe is fueling expanded adoption of the cloud and of the Datadog platform. Regarding our Q3 financial performance and key metrics, revenue was $1.2 billion an increase of 35 percent year over year, and above the high end of our guidance range. We ended Q3 with about 33.5 thousand customers, up from about 32 thousand a year ago We also ended with about 4.86 thousand customers with an ARR of $100 thousand or more. Up from about 4.06 thousand a year ago.

These customers generated about 91% of our ARR. And we generated free cash flow of $251 million with a free cash flow margin of 21 percent. Turning to product adoption. Our platform strategy continues to resonate in the market. 60-1 percent of our customers now use 4 or more products. Up from 54 percent a year ago. 40-1 percent of our customers use 6 or more products. Up from 31 percent a year ago. And 16 percent of our customers use 10 or more products, up from 8 percent a year ago. So we are landing more customers delivering value across more products through our unified platform.

1 milestone we are very proud of our infrastructure monitoring suite of products now exceeds $2 billion in ARR. Growing in the mid-30s percent year over year. Infrastructure monitoring was the first product we brought to market more than 10 years ago. Today, our customers rely on Datadog for visibility across their environments. Including cloud infrastructure, on-prem servers, containers, serverless applications, and GPU fleets. And while we are thrilled to reach this milestone, we still feel that we are just getting started. According to industry analysts, we are the market share leader in global observability, but with a market share percentage still in the teens, Moving on to R&D.

As AI changes how our customers build and run software, we have been investing and executing on 2 fronts. AI for Datadog and Datadog for AI. First, AI for Datadog. These are AI products and capabilities that make the Datadog platform more useful and increasingly automated for our customers. First off, our flagship agent. Meets AI, is off to a very strong start. Since we launched Bits, it has become the fastest adopted product in our history. And in October, Bits testing became generally available. With a single click, it discovers the critical user flows in an application. And generates tests to cover them.

Meanwhile, our AI research team is hard at work pushing the boundaries of what Bits and our platform can do. Last month, we introduced Scout, our own reasoning model specifically optimized for SRE tasks. Scout was built using our in house distributed reinforcement learning infrastructure, and now powers Bits' investigations. This is exciting for 2 reasons. First, it performed on par or even better than frontier models. On alert investigations. At a fraction of the inference cost. This is valuable, as it allows Bits to investigate many more alerts, start earlier, and go deeper on incidents As an always on first responder for our customers.

Second, this is also exciting as a demonstration of what we can achieve when we train models with our own production data. Critical user workflows, and internal tooling. Scout is only the beginning. With the Adaptive ML team, which joined us in June, we are broadly accelerating our work on reinforcement learning and model post training. Over time, we expect our research efforts to drive step change in capabilities across our platform. And move us closer to our goal of autonomous operations. Moving on next to Datadog for AI. These are the products that deliver end to end observability and security across the new AI stack.

As AI moves from experimentation into production, customers, today need to answer 3 key questions. Howard is it performing? Is it secure? And what is the ROI, or is it. Was that spent? And because AI does not run-in isolation, they want these answers on the same platform as the rest of their tech stack. On performance, we cover the whole AI stack from compute all the way to agents. GPU monitoring now goes beyond hardware reliability. With new capabilities that help customers maximize fleet utilization increase training throughput, and serve inference at a lower cost per request. The number of GPU devices monitored is up by a factor of 8 so far this year.

And some of our Frontier lab customers are working with us as design partners. On these capabilities. Going up the stack, agent observability helps teams monitor and evaluate their agents in production. Including the quality of their responses. Latency, token usage. The number of spans sent to agent observability is now up by a factor of 9 this year. We have also made agent observability available in the Anthropic marketplace, and we are a launch partner to OpenAI's new enterprise marketplace as well. So now customers can buy our product directly through the frontier labs they already work with. On the security side, our AI Guard product discovers the custom agents running across an organization and protects them. At runtime.

Against prompt injection and data exfiltration. Doing so on the same real-time data we collect to observe them. And on the cost side, companies everywhere are grappling with the very fast growth in token and AI spend. Beyond the 3 major clouds, AI providers are now the largest category of spend that our customers monitor. In Datadog cloud cost management. Or CCM. Now with AI cost in CCM, customers can see which teams, models, and agents drive their AI spend. Can govern that spend with budgets, forecasts, and anomaly alerts. More than a third of our CCM customers now track AI costs The number of customers doing so is up more than 11x, Moving up the stack again.

Our agent console adds to our CCM data, shows exactly which agents are actually used. By whom, for what task, at what cost. And most importantly, with what works. As we continue to innovate across AI for Datadog, and Datadog for AI, independent research is taking note. Forrester named Datadog a leader in its 2026 Forrester Wave for AIOps platform. And Gartner's August 2026 AI vendor-raise report, called Datadog the company to beat for observability platform. We believe this validates our leadership in observability as well as our ability to help customers manage complexity at the edge of AI. Let's move on to sales and marketing. And look at a few of the deals our go-to-market team closed.

It has been a very strong quarter. First, we signed a 7-figure annualized expansion with a leading software company. More than doubling the annual commitment. This customer is moving AI agents into production, and will replace the stand alone commercial tool with Datadog agent observability. To monitor their costs and quality. And by using FlexLogs, can gather and correlate larger log volumes at a lower cost. In total, they will consolidate 3 tools onto Datadog and go to 20 Datadog products. Including OnCall and Bits AI. Next, we landed a 5-year, 7-figure annualized deal with the US Federal Defense Agency. This customer runs thousands of cloud accounts, across hundreds of mission owners.

And as we have modernized, their manual tooling left them with fragmented visibility and security blind spots. They chose Datadog as their enterprise observability and security platform. And we will start with 9 products. Including infrastructure monitoring, and cloud security. I want to congratulate our public sector team on this milestone win. Which was a direct result of our investment in government. And the FedRAMP high certification we secured earlier in the year. And we are actively expanding our government platform to support workloads up to Impact Level 5 or IL 5. The highest level of accreditation for unclassified data. Which is part of our long term investment in the U.S. public sector.

Next, we landed a 7-figure annualized deal with a leading home health care software company. As this customer has grown, fragmented tools have slowed incident response and hurt customer experience. They will consolidate 4 tools into Datadog. Replacing their legacy log and security vendor with FlexLogs and Cloud SIEM, and relying on our sensitive data scanner to help them stay HIPAA compliant. This customer will start with 9 Datadog products, including incident management and Bits AI for faster resolution. Next, we signed a 7-figure annualized expansion with a leading online entertainment company. This customer is seeing a surge in traffic with their peak season, is scaling effectively with Datadog. Very easy. Their own AI agents already use our MCP server.

And today, they are also adding Bits AI. This customer will grow to 23 Datadog products. Including agent observability, and cloud cost management. Next, we are landed 7-figure annualized deals with 2 of Japan's most important banks, as they modernize their core financial systems, both banks are modernizing on Datadog for unified visibility across cloud and on-prem environments. Both customers are adopting the full Datadog platform. Including all 3 pillars, digital experience, security, software delivery, service management, and Bits AI. As they move towards AI driven operations. Next, we landed a 6-figure annualized deal with a top European AI lab.

This customer's developers were losing valuable time to slow bills and flaky tests with their ad releases in a highly competitive market. They chose Datadog CI visibility and test optimization. Replacing a commercial tool and freeing their own engineers to focus on shipping product. Their engineers also use Bits AI to easily build monitors and custom analytics. Next, we signed a 7-figure annualized expansion for an 8-figure annualized deal with a leading AI coding company. This customer is growing rapidly. And renewed early to keep pace with their expanding footprint. With this expansion, they will adopt flex logs and our new infinite cardinality metrics, to scale cost effectively with volume.

And they are also 1 of the largest users of our MCP server. This customer will grow to 13 Datadog products. Finally, we signed an 8-figure annualized expansion with the AI division of 1 of the world's largest technology companies which nearly tripled the annual commitment. Just 2 quarters after we landed them, they are scaling Datadog across a large number of AI training workloads. They are also adopting APM and Bits AI to further reduce engineering friction. Alongside these wins, on the other side of the customer spectrum, we are leaning into the large number of new AI-native companies we see created every day by investing in our Datadog for Startups program.

We are broadening its reach to a larger number of startups that are earlier in their life cycle. And offering deeper engineering collaboration with the ones that are seeing explosive growth. This is a truly exciting time to partner with startups. Now before I turn it over to David for a financial review, I would like to say a few words on the longer term outlook. There is no change to our overall view that digital transformation and cloud migration are long term growth drivers for our business. And AI is another large growth driver for us today.

As AI workloads moving to production drive more cloud consumption and more usage of the data platform, and we see that today across our business. All 15 of the top 15 AI leaders are our customers. And more broadly, we now serve over 850 AI customers up from about 750 last quarter. And across our broader customer base, we also see AI activity growing substantially. About 1-third of our customers now use 1 or more of our AI products. Including 3 quarters of our $100 thousand-plus customers with many of these customers still just getting started with these products. Leaving a large opportunity for future expansion.

And we continue to see rapid growth in agentic activity, with a number of MCP tool calls up by a factor of 50 so far this year. We are tremendously excited. About where this is going. As AI writes and runs more of our customer software, they are shipping more code faster across a growing mix of models agents, and infrastructure. Of course, to generates complexity that is very quickly outpacing what even the best engineering teams can track on their own. And that is exactly the problem we are investing to solve. AI for Datadog takes on more of that work for them, Datadog for AI helps them run their AI with confidence.

And our AI research turns our data into models that will deliver more autonomy over time. Because of that, now more than ever, we feel ideally positioned to help customers of every size and every industry as well as every type of user, 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: Thanks, Olivier, and good morning to everybody. Our Q3 revenue was $1.197 billion up 35 percent year over year. We continued to see robust usage growth from existing customers as well as a strong ramp in our new customers. Revenue grew accelerated within our broad base of customers, excluding AI customers. customers to the high-20s percent year-over-year, up about 1 percentage point from last quarter. And up from about 20 percent in the year ago quarter. And we saw robust growth with broad based strength across customer segments, spending bands, and industries. And excluding our largest customer, revenue growth accelerated again.

As we communicated in August, our largest customer reduced their usage in the third quarter, while their usage remained above their minimum commitment. Meanwhile, our AI customers' growth continues to significantly outpace the rest of the business. This over 850-strong group continues to diversify and grow. Including 39 customers spending more than $1 million annually with us. And we continue to land larger and grow faster with new logos, contributing more to revenue growth. The portion of our year over year revenue growth that relates to new customers was about 30 percent in Q3, up from 25 percent in the year ago quarter. And now regarding our retention metrics.

Our trailing 12 month net revenue retention percentage was in the low 120s percent similar to last quarter. And churn remains low with gross retention stable. Gross revenue retention stable in the mid- to high 90s. We believe this metric highlights the mission critical nature of our platform for our customers. And now moving on to our financial results. Billings in the quarter were $1.16 billion up 30 percent year over year. Remaining performance obligations or RPO was $3.85 billion up 38 percent year over year. And current RPO grew in the mid-30s percent year over year. RPO duration increased slightly year over year.

As a reminder, we continue to believe revenue is a better indicator of our business trends than billings or RPO. 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. For Q3, our gross profit was $972 million and the gross margin was 81.2 percent. This compares to a gross margin of 79.6 percent last quarter and 81.2 percent in the year ago quarter. Q3 OpEx grew 25 percent year over year versus 26 percent last quarter and 32 percent in the year ago quarter.

And Q3 operating income was $334 million or 28 percent operating margin, compared to 23 percent last quarter and 23 percent in the year ago quarter. And now turning to the balance sheet and cash flow statements. We ended the quarter with $5.2 billion in cash, cash equivalents, and marketable securities. Cash flow from operations was $290 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $251 million with a free cash flow margin of 21 percent. Quarterly, the timing of collections and payments vary. And on a trailing 12 month basis, free cash flow margin was 26 percent.

And now for our outlook for the fourth quarter and for fiscal year 2026. First, our guidance philosophy overall remains unchanged. As a reminder, we base our guidance on trends observed in recent months, and imply conservatism on these growth trends. Regarding our largest customer, our Q4 guidance contemplates their revenue at their minimum commitment. Now for the fourth quarter, we expect revenue to be in the range of $1.19 billion to $1.2 billion which represents a 25 to 26 percent year over year growth. Excluding our largest customer, Q4 revenue growth implied in our guidance is in the low to mid-30s percent range.

Non GAAP operating income is expected to be in the range of $280 million to $290 million which implies an operating margin of 24 percent. The fourth quarter includes large marketing investments including AWS Reinvent. Our non GAAP net income per share is expected to be in the $0.69 to $0.71 per-share range, based on approximately 379 million weighted average diluted shares outstanding. For the fiscal year 2026, we expect revenue to be in the range of $4.515 billion to $4.525 billion which represents approximately 32 percent year over year growth. And non GAAP operating income is expected to be in the range of $1.09 billion to $1.105 billion which implies an operating margin of 24 percent.

Finally, non GAAP net income per share is expected to be in the range of $2.73 to $2.75. Based on approximately 375 million weighted average diluted shares outstanding. Now some additional notes on our guidance. We expect net interest and other income for the fiscal year 2026 to be approximately $200 million We expect cash taxes to be about $30 million to $40 million and we continue to apply a 21 percent non-GAAP tax rate. For 2026 and going forward. And finally, we expect capital expenditures and capitalized software together to be in the 3 to 4 percent of revenue range in fiscal year 2026. To summarize, we are pleased with our execution in Q3.

Which combines strong growth with strong profitability. We continue to invest to capture our long term opportunity in the cloud and in AI. And I wanna thank Datadogs worldwide for their efforts. Now with that, we will open the call for questions.

Operator: Operator, let's begin the Q&A. Thank you. and wait for your name to be announced. To withdraw your question,, please press 1-1 again. Please standby. We will compile the Q&A roster. 1 moment for our first question. Our first question will come from the line of Sanjit Singh from Morgan Stanley. Your line is open.

Sanjit Singh: Yes. Thank you for taking the questions and congrats on the strong results on Q3.

David Obstler: David, I want to start with the Q4 guide. If we went back 90 days ago, have anything changed respect to underlying assumptions between the different cohorts, so between the core business and the sort of largest customer versus 90 days ago, or is this outlook, just sort of a reflection of that sort of minimum commitment methodology that you are applying in Q4? Just wanted a sense of has anything changed with respect to the outlook? David Obstler: There are a couple-- yeah.

Sanjit Singh: Thank you. There are a couple of things to talk about.

David Obstler: First of all, I think we said versus our largest customer that they renewed, and they were spending above the minimum commit, and that happened in Q3. We said that in our guidance, we are going to only include the minimum commit despite the fact that in Q3, they were spending above the minimum commit.

Olivier Pomel: As we talked about, we have accelerated in Q3, excluding the largest customer. So I would say the only thing that has changed was that in sort of Q3,, we did some renewals extensions with some large and large AI, customers where as normal, we essentially extend and increase the commit but do better pricing for that. And that, that happened in a number of large customers. it is our normal behavior, and that is the. Really, the only thing that changed since our last communication. Oli, do you want to add anything? there is a few you discussed on the call. I could bring up a few of those large customers.

Where we saw renewals way earlier than we thought we would. there is another 1 that happened in October that we are now discussing. So these are all good news, but, they do create, like, headwind into the short term into the revenue. And, that is 1 change from last quarter. Other than that, like, everything else played out pretty much to where we were expecting. With some happy surprises including in particular, the continued acceleration of the non-AI customer,, which has been very good overall.

Sanjit Singh: Understood. And then if we think about the x AI native customer growth, that is been on an accelerating trend for several quarters now. As we think about this you know, this core non AI native business, you obviously have a lot of tailwinds on the AI portfolio. It seems like the $100 thousand customer cohort is definitely more of the AI product. Do you feel that, that can continue to drive or sustain that trajectory? Or are we, after multiple quarters of acceleration, do you sort of see a leveling off in that in that xAI data cohort? Thank you.

Olivier Pomel: I mean, look. In the long term, all of these customers are adopting AI, and they are consuming more cloud, and they are building more software and they are changing the way they work. So we expect a long period of growth for these customers. Now with the-- would the overall growth rate ease? You know? Does it keep going up a lot? Does it stay stable? You know? it is a very high growth rate already, and we feel pretty good about it. it is hard to say where it is going to go. But we are very confident in the broader trend that is underpinning that. Alright. Thank you.

Operator: 1 moment for our next question. Next question will come from the line of Gabriela Borges from Goldman Sachs. Your line is open.

Gabriela Borges: Hi, good morning. Thank you for taking the question. David, I want to stay on this point on the AI-native cohort. Because I think you have been very transparent in how you talk about your customers versus the rest of the cohort. And my question for you is, you have these funky sequentials, because you have had this incredible growth rate in this cohort over the last 12 months, and now you have got this renewal dynamic with your largest customer, you are also talking about the renewal dynamics on the early renewals. So my question for you is, what can you tell us about how to think about modeling the sequentials in this business?

Is it that we get through the next, call it, 2 quarters and then we are back to steady state? Is it that maybe we see another step function down every year in the sequential because you have this earlier renewal dynamic, which again is a really good place to be because you are getting these really large deals that the only way to get better pricing is because the dollar volume is going up. So maybe just to put some shape to how we think about the sequentials for the AI-native cohort and when we will be back to something that looks like normal, which I get is a tricky definition of the word normal anyway.

David Obstler: Yeah. So good question. So in terms of the largest customer, I think as we said, the customer, renewed. The customer. We decided, to put only the commitment, the minimum commitment. So the customer in Q3 spent substantially above the minimum commitment. So 1 thing that is very important in the sequentials is what happens there.

Olivier Pomel: We do not control that. The customer you know, cannot go below the minimum commitment, but can be various degrees above the minimum commitment. So that is, that is 1 factor that we. So, essentially, 1 thing that will create less of a sequential guidance or actual would be that number stabilizes and is not running above the minimum commitment. So that is 1. I would say, you know, Gabriela, it-- Yeah.

The point here is that 1 part that is very usual, which is that we have all these customers that are pending and they are growing and they are renewing, and then when they renew, they get a better deal, and they also adjust their traffic over time on the. With an underpinning of growth, which is what we have seen since the beginning of the company. there is something that is new that we have some of these companies that are scaling incredibly fast to a level that we have never seen in the industry or any industry really before, which creates new dynamics.

You know, this is why we can be a bit surprised when we see a customer renew early just a few quarters into their starting to adopt the product. In general, though, we are getting a larger number of these customers, like the AI-native cohort is broadening. We have many more customers. We need more large customers into that cohort. We called out a few wins that we landed another AI lab, for example, this quarter that is in the notes. So we in the long term this is going to broaden. This is going to represent a much larger fraction of our customer base. And as a result, there will be more stability over time.

I still do not think it is a very unstable business. I mean, we are talking about the chance of a 2 percentage, you know, quarter to quarter in terms of where we where we end up versus where we think we are gonna be. But, you know, we think at the end of the day, the. This is a usage based model, and we get visibility into the mid- to long-term than we do into the very short term.

David Obstler: I do wanna remind everybody that all things being equal, the sequential between Q3 and Q4, tends to be less because of, the late December effect. When our usage based products and vacations so and some code freezes. So, that could be transparent based on the other factors or it might be underneath of that. But we do have that effect in Q4, which we do not have in the other quarters.

Gabriela Borges: These are really helpful comments. Thank you.

David Obstler: Yeah. Thank you. Thank you.

Operator: Our next question will come from the line of Raimo Lenschow from Barclays. Your line is open.

Raimo Lenschow: Thanks. Can I speak on 1 last time. like, 1 last question on this 1?

David Obstler: Yeah. So if I think about the customers renewed early, so they get better pricing. Mm-hmm. But, the volume is going. Kind of going crazy there, but it kind of creates a short term headwind because the same volume is now on a lower price. But then the volume grows. And from then on, in a couple of quarters, we should see like the normal progression. Is that kind of in the right thing to think about? I think we have that in the past, but, like, just wanted to clarify that. And then have 1 follow-up.

Olivier Pomel: Yeah. that is that is totally what happened. there is a few other things that typically happen with situations, you know, which is try to understand what it is they are going to need. So they look at what they are using, and they think, okay. This part, maybe we do not need as much of. This part, we are probably more of. Or maybe some situations, we tell them, hey. Maybe you should use this different tier of retention for this type of data because of the way you are using it. You know? So there is a bit of account optimization there when we work with them. But the tech DCV is on the background of growth.

These customers are growing, and they are deploying much larger workloads, and they are looking at doubling, tripling, quadrupling their footprint with us over time. And this is basically what we are working on with Okay.

Raimo Lenschow: Perfect. And then the main question for me was on the xAI part, like, historically we would think about, that growth coming from cloud migration people modernize and doing more stuff. You mentioned another aspect, which is kind of because of AI, etcetera, we can code more. there is more application modernization, all this stuff happening. So it is kind of changing in the way it is kind of evolving for you from, you know, classically, with people modernizing, they have to go to Datadog. Thank you very much. Well, now, actually, the companies are working differently and that is also starting to drive the xAI business. Can you speak to that, please?

Olivier Pomel: Yeah. I mean, the well, there is 1 thing I would mention on the call that is always true. We also do more things for our customers. You know, we are what a lot of what they were using. We have more products to cover more of the surface area. As it comes to customers building more with AI, like, it also brings some of our other products more into the. Like, front and center. You know? So 1 example is customers are building a lot more software. So they are putting a lot more pressure on their CI and deployment and testing. And so they are trying also to adopt more of our CI, products. We.

I think we called out a few wins around that too in the script. So that opens the door for more products beyond core observability. For us. Yeah. Okay. Perfect. Thank you. Well done. Thank you.

Operator: Our next question will come from the line of Samik Chatterjee from JPMorgan. Your line is open.

Samik Chatterjee: Hi. Thanks for taking my questions. And I had a couple. Maybe for the first 1, I know you mentioned your largest customer did spend above their commit in Q3. There any way to tell us how much of the beat relative to your guidance in Q3 because of the large customer spending above the commit rate versus the rest of the business, depending on how you forecasted them in Q3? And I have a quick follow-up.

David Obstler: Yeah. We are not-- we are not answering. We are not being specific, but it is true. It-- It is true that if you are basically guiding, like, below their-- We said we would be conservative in Q3. We said conservative relative to what they were spending. And in Q4, we have been explicit and said minimum commitment. It is true that if they spend more than that, that would be an implicit beat, mathematically.

Olivier Pomel: Yeah. Yeah. But we. Yeah. We did not change the assumption there over time. Yeah. We kept everything unchanged. I think the 1 different-- we brought up earlier, the 1 difference in the. Since the last quarter is some positive trends on the on the overall customer growth, but also some early renewals of larger customers, which includes some other AI customers. that is the ones that we are, like, we should remember as being the difference. Yep. Sure.

Samik Chatterjee: And so my follow-up. I mean, look. The contribution of revenue from new customers continues to ramp up steadily. Can you just talk about sort of what is the average land size that you are seeing now with new customers and how that has changed? And what sort of behavior you are seeing on that front, how you are thinking about it as you sort of move into next year in terms of how much new customers contribute to your overall growth? Thank you.

David Obstler: We have not provided guidance on what we said. Over time, we have seen an increase of average land size due to, the increase of the platform. And as we give the metrics on the adoption of products, the adoption of more parts of the platform. Oli, do you wanna Yeah.

Olivier Pomel: I think. But, look, this is something we see in the customer base that we see that, 1 is on. With the new AI-native companies, some of them start with more scale earlier. Like, we have discussed it before, but, you know, we see companies scaling faster than they used to. So they represent more of the revenue faster. But we also are developing new segments and landing large customers you know, from day 1. You know? So we called out a few on the script today. We mentioned, I think, a defense agency in the U.S. government. We mentioned Japanese banks, like, 2 of the 3 largest Japanese banks in the same quarter.

Those are all sizable low land deals. And if you compare the business to what it was, say, 5 years ago, like, this is definitely a new side of the business that did not necessarily have before. And by the way, those wins I mentioned, they do not they do not even register into the revenue yet, you know, because these are these are plans, but these are not customers that are ramped users yet. So, Thank you.

David Obstler: Thank you.

Operator: Our next question comes from the line of Matthew Hedberg from RBC. Your line is open.

Matthew Hedberg: Great. Thanks for taking my questions, Appreciate it. You know, I wanted to start with training. You noted in your script, you had an 8-figure, I think, annualized expansion with an AI division of a large software company. Sounds like they are using you guys for AI training workloads. I guess, you know, this is. To us, it seems like a really interesting growth opportunity. Can you step back and kind of talk about what that training opportunity is, and what could it mean for your growth?

Olivier Pomel: I mean, it is been evolving positively. You know, if you, look, you know, a year or 2 years ago, basically, nobody was doing training. It was only the. Only a few front labs were doing their own training, and nobody else was. Now we see an order of magnitude more large-ish labs or new labs that are doing training. To large scale. And then there is another whole category of companies below that are starting to do their own. You know? Including us. You know, we started doing training at a decent scale, and, you know, we showed some of the results. We just discussed some of the results on the call.

So, I think there is some interesting potential development there where training becomes a part of everybody's business and everybody's customizing model and building their own. And so we are investing. I would say, similarly, to call it a mass market yet. Right now, we see some interesting indications of it. We also see that we can be extremely useful to some very large customers. But I think we still have to invest, still have to build. We still have to feel that market develops.

Matthew Hedberg: Got it. And, actually, David, if I can just have 1 more follow-up. You know, on your largest customer, you used the word substantially above their minimums, which I find interesting. And I know you are assuming, you know, only the minimums for Q4, but philosophically, looking at this customer and their recent consumption patterns, does this perhaps change your opinion about how they might use Datadog longer term, maybe new use cases above and beyond what they are using today that could make that growth opportunity really interesting for this customer?

David Obstler: I think that is true for any customer that, you know, there are many different use cases. For that in particular, we. As we said all along for many quarters, we do not control. We have the contract, the renewal, and we are not in the business of projecting that. But it is true from, you know, across our customer base, that there are many use cases in a company, whether it be by division, end user, etc. Oli, you want to add in?

Operator: Our next question will come from the line of Karl Keirstead from UBS. Your line is open.

Karl Keirstead: Okay. Great. Thank you. Maybe, Olivier, for you, you mentioned in your comments that a lot of customers are grappling with higher token costs, higher AI spend, and are trying to get it under control. I am just wondering based on all your customer interactions, how enterprise spending behavior is changing in light of the fact that they are trying to get their token costs under control. And to the extent that any of the customers that early renewed to try to get price discounts were not AI natives. They were traditional enterprises. Do you think that might be because they are trying to grapple with budget pressure? Thanks so much.

Olivier Pomel: Yeah. So I think, look, this is a very widespread problem. I think the we have seen is there is been an explosion of adoption of AI, in particular, coding agents. And that was not planned. Largely. Like, you know, a year ago, nobody had the amount they spend in their budget on AI. So they. Everybody has to try and figure out what that works, and part of that they actually need and what they get from that. So there is a strong need for tooling,, which is something we provide. it is a great opportunity for us, and we see you know, great growth and adoption of our cloud cost management product for that, or our console.

These are our products. Now, in terms of the early renewal, that we see, they are not directly related to that. I think they come from large customers. That are either AI labs or that are focused on the. That are tech companies in general, I would say. And the. And that is that is does not really have anything to do with their own token consumption. It has more to do with their own growth as a business. Okay. Thank you.

Operator: Question will come from the line of Fatima Boolani from Citi. Your line is open.

Joel: Hi. Good morning, everyone. This is Joel on for Fatima. I will maybe just ask 2 real quick. So firstly, just on margins and operating and gross margin leverage, maybe for you, David. you know, strong results this quarter, at least relative to our expectations, but we do know there are also certain areas that you are investing fairly heavily in. So just curious what some of the drivers of the performance were, specifically this quarter, and then what you are updated or what your latest investment posture is? And then just the quick second question, I will just throw it in now is any data points on Bits AI kind of specifically in the customer base? Thank you.

David Obstler: Do you want to go? Yeah. I will go first. Yeah. it is very similar to what we said. We are investing aggressively in the go-to-market., we continue to have white space. Uncovered, both from a geographical or an end-market, for instance, Fed. And we are continuing to, be successful in scaling our quota capacity and, and you see that in the growth. In addition, we are continuing to invest in R&D. In this case, it is combination of AI, of investments, in a combination of AI and people. We see lots of opportunities.

I think Oli will now go talk about the products,, but we continue to see a lot of opportunity for us to get market share get platform penetration, and increase our, you know, feet on the street on a global basis.

Olivier Pomel: You want to go over Bits? Yeah. On Bits, I mean, we have mentioned on the call, we see amazing adoption of it. Like, this is when we look at the number of individual customers or users that are using it. This is-- it is been the strongest launch we have ever had as a product,, so we feel very good about it. We have also revamped the way we package it over the past quarter. So, you know, we used to charge specifically for investigations Now we have a model of AI credits that cover a whole different set of use cases that involve Bits as our agent. And, you know, we have seen great results thanks to that.

So I would say, super encouraging. I would say it is still early because there is still so much that we were building into it. So many things our customers want us to do for them, So many opportunities to, automate their workflows. But we are. We feel very, very good about the current status.

David Obstler: Let me just circle back on 1 thing. I think I neglected to go over gross margins, which you mentioned. I think it is very similar to what we have said before, that through a combination of the penetration of our platform as well as our engineering we have been, you know, plus or minus in this 80% range. I think you see in this quarter, we ended up around 81. And so this is common practice for us. We invest, optimize, do engineering projects, and the what was communicated in gross margin indicates stability there, which we have evidenced in previous quarters. Thank you.

Operator: 1 moment for our next question. Our next question will come from the line of Kirk Materne from Evercore ISI. Your line is open.

Analyst: Thanks very much for taking the question. I guess, David, on. Or and Oli, on this native AI cohort that is renewing a little bit early, Yeah. Obviously, they are coming back because they are spending so much with you all and obviously wanna get better economics. I am just curious, what is the sort of attach rate on some of the newer renewals? When they come back and renew early, do you still have time to sort of up-cross-sell them some of the other products? I guess, what are you seeing in that dynamic?

I realize it creates some near term headwinds from a revenue perspective, but it would also, I think, open up the opportunity to have a discussion about you expanding their usage across multiple products. Thanks.

Olivier Pomel: Yeah. I mean, look. If we. If you look at these cohorts, and if you compare them in particular to the planative cohorts you might have had, like, 5 or 6 years ago, like, these are adopting many more products. Know? So we have seen much broader product adoption. Platform adoption. With those customers than, you know, we saw, like, 5, 6 years ago. So that. that is definitely something that is happening. Some of that is happening at renewal time. Sometimes it is just happening organically, you know, as new needs emerge and always spread to more teams, you know, inside those companies.

David Obstler: But I think it is important to clarify. These are, renewals, but they are at higher levels than their previous commit, which is how they are getting you know, the volume discounts. But we are we are renewing these customers at higher commits. Okay. And, David, I guess just for you, I mean, this would seem this cohort up for renewal earlier. it is all good over the long term. I would imagine just visibility is a little bit harder from a sequential perspective. Is that sort of to Oli's comments on the long term, I would imagine it is all good from a longer term per.

Perspective, but it just creates a little bit more volatility for you on a on a sort of quarter out guidance perspective. Is that fair? I would say it has a small, you know, small amount of variation in, the timing of the revenue path of these customers. The sequential here, was really 3 things. It was really, 1, that factor. 2, I think we mentioned the largest customer in the guide, which, know, we are not predicting what they are gonna do. And 3 is we know about the small amount of seasonality. So it is 1 of the factors. We try, you know, to get a good sense of that.

But, of course, you know, as our RPO and as our contract extend, it does create some volatility around the edges in the sequential nature.

Olivier Pomel: But again, it is all good news. Look, we are using a usage-based model, and when we deal with fast-growing customer, these customers themselves do not know exactly how they are going to grow over the next few months and what part of their business and what. how their consumption is going to change. So we are we are happily work with them and make sure that we support their growth and, you know, we do everything that is right. The only time we think about it in difficult terms is when we prepare for an earnings call, because we have to figure out, okay. So, you know, what part can we communicate? What part do we know?

What part do we not know? You know? But, overall, they are all great news. Great customers. Great growth. A great adoption. Amazing trends that are underpinning the industry as a whole. We feel very good about it.

David Obstler: Yeah. And this is similar to what we have done. In the company's existence, that you talked about I mean, our motion is to, increase the commitment of customers broaden the platform, and extend the contract. So that is what we do all the time. It has some variability, but it is very similar to what we have been doing in the company throughout its history. And it is a good thing.

Analyst: Super. Thanks very much.

Operator: Thank you. Our next question will come from the line of Peter Weed from Bernstein Research. Your line is open.

Peter Weed: Thank you so much. You talked about it a little bit, but it is really exciting to hear about the release of the Bits AI Scout and the strong early adoption. Could you help enumerate that a little bit and maybe remind us how the pricing works there? Is it kind of directly monetized, or does it really drive the adoption or use of Bits AI overall? It would be really exciting to kind of appreciate how broad Bits AI adoption is now and how it might move the needle on overall growth and perhaps become another major pillar for the business.

Olivier Pomel: Yeah. So it is actually a bit of both. You know? So look, it normally sits on all of the data we acquire, the way we can process it, and all the various products we have. You know? So that is the core. But we also have a model where we charge for AI credits specifically for what Bits does on top of that. And the model we have chosen is a model where we stay fairly close to inference cost there so that we can pass on the savings,, you know, as the models become more efficient. To our customers and so we can run many more investigations.

The reason we have done that is the field is changing so fast, and we are improving the efficiency of those models so fast. That, you know, it is it is not very productive to have a fixed price on the specifics as you expect the, the volume of things you do to increase by orders of magnitude while the process is down by orders of magnitude as well. And so we chose to be on that mode, which works really well. So long term, we expect both to monetize this directly as we do today. And to drive broader adoption of the platform and to add more value to our customers.

In terms of what becomes the dominant monetization, it is hard to tell. And we do not really care. Again, we are fully user based. You know? So as long as we get more usage, deliver more value, we will be fine. Thank you.

Operator: Thank you. Our next question will come from the line of Koji Ikeda from Bank of America. Your line is open.

Koji Ikeda: Yes. Hey, thanks, guys. I wanted to ask a question on infrastructure monitoring. So guys disclosed it is now exceeding $2 billion in ARR and growing in the mid-30s. Could you quantify how different that growth rate is from when you last disclosed the scale of this business at $1.5 billion earlier this year. How much of that growth is coming from AI infrastructure and how sustainable is that growth rate as GPU efficiency improves? Thank you.

Olivier Pomel: So, I mean, look, we have seen acceleration of the business in general. And not just thanks to AI and EC2, but also thanks to the rest of the customer base. So we mentioned that. You know, and that applies to infrastructure monitoring also. Today, that growth is not actually related directly to GPUs. The you know, we. GPUs are still largely unseen for our customers on training. We have products there, but we do not monetize those products widely yet. And so for us, that is an opportunity we have in front of us. that is not something that is baked into the current growth and the current numbers. Thank you.

Operator: Our next question will come from the line of Patrick Edwin Colville from Scotiabank.

Patrick Edwin Colville: Your line is open. Thank you so much for having me on. To me, 1 of the most interesting points in the prepared remarks was about data dog training, your own, you know, front-end models. And then, Olivier, your comments around, you know, this behavior is not mass market, but seeing how the market develops. I guess I wanna ask you because, you know, you. You are at the forefront of you know, how things are changing. How does this affect Datadog's use of proprietary tokens internally or for customers versus open weight tokens?

And then I guess David, if this shift that, you know, Olivier is describing picks up steam, what might that mean for Datadog profitability, which nicely increased this quarter, but is there potential that this might help the bottom line as well? Thank you.

Olivier Pomel: So look. What we see internally and what we see with customers is it is a there is a mix of models that are being used. So, you know, I think I can I can say we and include both customers and us into that collective we, because the behaviors are you know, fairly similar? We are big customer. We are big consumers of the top labs' top models, but we also consume open weight models. A growing amount of open-weight models, and we train and post-train on them. Yeah. I think it is probably representative of where the industry as a whole is going.

We do that because there are certain things you can only get from the top of the line models that are being generally trained by others. there is a number of things that are good enough and very cost efficient with open weight models. You can just pick up and run. And there is the. Finally, a number of other things that you can make way more efficient with your own data, your own workflow, your own post training, which is what we have done with some of the models we announced. So I expect the. That mix to remain, you know, about the same.

In terms of the impact on the on margin, look at it, you know, today, you get some control on what you spend your money on. Like, if it is very proprietary to you, and you can deliver superior economics, you should absolutely do so. If not, like, if it is very undifferentiated and applies broadly in some parts of your business that are similar to the needs of other businesses. Then it is fine probably to use off the shelf model and keep consuming those. that is okay.

David Obstler: Thank you.

Operator: Thank you. Our next question will come from the line of Aleksandr Zukin from Wolfe Research. Your line is open.

Aleksandr Zukin: Hey, guys. Thanks for getting me in. Oli, maybe a quick 1 for you and David, a follow-up. To what extent are you seeing the demand for agent observability from both AI natives? And the core driving demand for offerings like your Traces product? And what is the. Like, what has changed here longer term just given the kind of elevated adoption levels? And maybe also any comments on, cybersecurity and the plans there?

Olivier Pomel: Yeah. So we see we see growing demand for agent observability. And what is-- so there was always interesting demand from the, you know, the AI-native labs,, etcetera, but what is most interesting here is the non-AI-natives are consuming more and more. And because they are getting deeper into AI, we see real demand, real volume, and really exciting-- a really exciting pipeline for the product, actually. So we think that this is like, crossing the chasm, so to speak, and that is that is exciting for this product. Even though we did not mention any hard numbers today on that. On, on cybersecurity, look. there is so many new opportunities with AI.

We are very happy to see that we can land the full platform with large customers. You know, we mentioned that for the defense agency. We mentioned that also for. In a couple of other wins when we landed not just observability, but, our security platform at the same time. So we feel good about the market fit there, and we feel good about the way the whole retooling and rebuild that is taking place with AI is playing in our favor. Perfect.

Aleksandr Zukin: And then, David, maybe for you just a clarification. If we think about-- First of all, it is great to hear about the renewal with your largest customer. And I think we totally understand the guiding for the minimum for Q4. Any pearls of wisdom on how to think about modeling that for 2027? Because presumably, that is a 1 year contract. And is that something we should think about kind of maintaining at some level? Is that something that you guys are gonna take out of the model?

And then if I think about how you are guiding, with respect to some of those other early renewals, for the AI natives, that get renewed, Are those also at the minimum commitment level? Are those slightly above?

Analyst: Just any help on those 2 items, I think, would be helpful for Yeah.

David Obstler: Reminding everybody. Yeah. Yeah. On that, we will give guidance for 2027 on our year end call. You know, I think, we have said in the last couple quarters that we are guiding at whatever the contractual minimums, and, you know, we have repeated that this quarter. We will give some more flavor on that when we do 2027. And in terms of the rest of the business, what we have been. What we do is. And we have said this over the many years, is we take the growth rate of those customers and we discount that.

So that would be how we apply it across the customer base, in taking the most recent organic growth and then discount it. And we would certainly be doing that for both the early renewal customers and our broader customer base.

Olivier Pomel: Thank you, guys. Alright. And I think that is that is that is it for our questions today. So thank you all for listening. I also want to thank everybody at Datadog for a great Q3. I know we are right in the middle of an even greater Q4, We are very, very busy. We are meeting. I am going to meet a lot of our customers at the, largest AWS conference in a few weeks. And you know, we hope to have many more, you know, exciting product announcement to share soon. So thank you very much, all, and see you next quarter.

Operator: Thank you for your participation in today's conference. This does conclude the program. You may now disconnect. Everyone, have a great day.

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