Datadog Q3 2026 Earnings Call Transcript
Datadog (NASDAQ:DDOG) reported third-quarter financial results on Wednesday. The transcript from the company’s third-quarter earnings call has been provided below.
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Summary
Datadog reported Q3 2026 revenue of $1.2 billion, a 35% increase year-over-year, surpassing the high end of their guidance.
The company saw strong growth in both AI and non-AI customer segments, with AI customers driving significant revenue growth.
Datadog’s Infrastructure Monitoring suite has surpassed $2 billion in ARR, growing at mid-30s percent year-over-year.
The company highlighted significant early adoption of its AI products, particularly Bits AI, which has become the fastest-adopted product in its history.
Datadog continues to expand its customer base, ending Q3 with approximately 33,500 customers and 4,860 with an ARR of $100,000 or more.
The company completed several notable deals across various sectors, including government and major tech companies, indicating robust market demand.
For Q4 2026, Datadog expects revenue between $1.19 to $1.2 billion and for the fiscal year, revenue is projected to be $4.515 to $4.525 billion, reflecting a growth rate of approximately 32% year-over-year.
Management expressed confidence in long-term growth drivers such as digital transformation, cloud migration, and AI, despite some short-term variability due to early renewals by large customers.
Full Transcript
OPERATOR
Good day and thank you for standing by. Welcome to the third quarter 2026 Datadog earnings conference call. At this time, all participants are in listen-only mode. After the speaker’s presentation, we’ll open up for questions. To ask a question during the session, you will need to press star 11 on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, please press star 11 again. Please be advised that today’s call is being recorded.
I will now hand it over to our first speaker, AJ Lubitsch, Senior Vice President, Strategic Finance and Investor Relations. Please go ahead.
AJ Lubitsch (Senior Vice President, Strategic Finance and Investor Relations)
Thank you, Victor. Good morning and thank you for joining us to review Datadog’s third quarter 2026 financial results, which we announced in our press release issued this morning. Joining me on the call today are Olivier Pomel, Datadog’s Co-Founder and CEO, and David Obstler, Datadog’s CFO. First, I’d like to thank Yuka Broderick for her excellent work leading our IR function over the last five and a half years as she takes on a new role within Datadog.
I’m 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 of 2026, and related notes and assumptions, our product capabilities and our ability to capitalize on market opportunities. The words anticipate, believe, continue, estimate, expect, intend, will and similar expressions are intended to identify forward-looking statements 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 June 30, 2026. Additional information will be made available in our upcoming Form 10-Q for the fiscal quarter ended September 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, which are available at investors.datadoghq.com. With that, I’d like to turn the call over to Olivier.
Olivier Pomel (Co-Founder & CEO)
Thanks, AJ, and 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 strengths across our business including across our product lines and across our diverse customer base. Revenue growth from our non-AI customers accelerated for the fifth straight quarter and was in the high 20s percent year over year, up about 1 percentage point from last quarter and up from about 20% in the year-ago quarter.
Additionally, our AI customer cohort remains a strong growth driver. This cohort continued to grow and diversify both in the number of customers we serve and in 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% year over year and above the high end of our guidance range. We ended Q3 with about 33,500 customers, up from about 32,000 a year ago. We also ended with about 4,860 customers with an ARR of $100,000 or more, up from about 4,060 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%.
Turning to product adoption, our platform strategy continues to resonate in the market. 61% of our customers now use four or more products, up from 54% a year ago. 41% of our customers use six or more products, up from 31% a year ago. And 16% of our customers used 10 or more products, up from 8% a year ago. So we’re adding more customers and delivering value across more products through our unified platform. One milestone we’re 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 it for visibility across their environments, including cloud infrastructure, on-prem servers, containers, serverless applications, and GPU fleets. And while we’re thrilled to reach this milestone, we still feel that we’re just getting started.
According to industry analysts, we are the market share leader in global observability, but with a percentage market share still in the teens. Moving on to R&D, as AI changes how our customers build and run software, we’ve been investing and executing on two 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 Bits AI is off to a very strong start. Since we launched Bits, it has become the fastest-adopted product in our history. Any Observer Bits testing became generally available with a single click. It discovers critical user flows for 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 Scalps, our own reasoning model specifically optimized for SRE tasks. CAT was built using our in-house distributed reinforcement learning infrastructure and now powers Bits Investigations. This is exciting for two reasons. First, it performs on par or even better than foundational 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.
SCARP 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 a 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 three key questions: how is it performing? Is it secure? And what’s the ROI, or is it worth the spend? And because AI doesn’t 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 we monitor is up by a factor of eight 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, and token usage. The number of spans sent to Agent Observability is now up by a factor of nine this year. We’ve also made Agent Observability available in the Anthropic marketplace, and we’re 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, going on the same real-time data we collect to observe them. And on the cost side, companies everywhere are grappling with a very fast growth in token and AI spend. Beyond the three major clouds, AI providers are now the largest category of spend our customers monitor in Datadog Cost Management, or CCM. Now with AI cost in CCM, customers can see which teams, models, and agents drive their AI spend and 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 11 times year over year. Moving up the stack again, our Agent Console adds to our CCM data and shows exactly which agents are actually used by whom, for what tasks, at what cost, and most importantly with those outcomes. 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 platforms, and Gartner’s August 2026 AI vendor race report called Datadog the company to beat for observability platform. We believe this indicates leadership in observability as well as our ability to help customers manage complexity in the age of AI.
Let’s move on to sales and marketing and look at a few of the deals our go-to-market teams have closed in what has been a very strong quarter. First, We signed a seven-figure annualized expansion with a leading software company. More than doubling their annual commitment, this customer is moving AI agents into production and will replace a standalone commercial tool with Datadog Agent Observability to monitor their costs and quality. And by using Flex Logs, they can gather and correlate larger log volumes at a lower cost. In total, they will consolidate three tools onto Datadog and grow to 20 Datadog products, including Encore and Bits AI.
Next, we landed a five-year, seven-figure annualized deal with a U.S. federal defense agency. This customer runs thousands of cloud accounts across hundreds of mission owners, and as they’ve modernized, their manual tooling left them with fragmented visibility and security blind spots. They chose Datadog as their enterprise observability and security platform and will start with nine 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’re actively expanding our government platform to support workloads up to Impact Level 5, or IL5, 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 seven-figure annualized deal with a leading home healthcare software company. As this customer has grown, fragmented tools have slowed incident response and hurt customer experience. They will consolidate four tools into Datadog, replacing their legacy log and security vendor with Flex Logs and Cloud SIEM, and relying on our Sensitive Data Scanner to help them stay HIPAA compliant. This customer will start with nine Datadog products, including Incident Management and Deep AI for faster resolution.
Next, we signed a seven-figure annualized expansion with a leading online entertainment company. This customer is seeing a surge in traffic with their peak season and is scaling effectively with Datadog. Their own AI agents already use an 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 landed seven-figure annualized deals with two of Japan’s most important banks as they modernize their core financial systems. Both banks are standardizing on Datadog for unified visibility across cloud and on-prem environments. Both customers are adopting the full-stack platform, including all three pillars: Digital Experience, Security, Software Delivery, Service Management, and Bits AI, as they move towards AI-driven operations.
Next, we landed a six-figure annualized deal with a top European AI lab. These customers’ developers were using valuable time due to slow builds and flaky tests, with delayed 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 products. Their engineers also use BCI to reliably build monitors and custom analytics. Next, we signed a seven-figure annualized expansion for an eight-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 add Flex Logs and our new Infinite Cardinality metrics to scale costs effectively with volume, and they are also one of the largest users of our MCP server. This customer will grow to 13 Datadog products.
Finally, we signed an eight-figure annualized expansion with the AI division of one of the world’s largest technology companies, which nearly tripled their annual commitment just two 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 large number of startups that are earlier in their lifecycle 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’d 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, from about 750 last quarter. And across our broader customer base, we also see AI activity growing substantially. About a third of our customers now use one or more of our AI products, including three quarters of our $100,000-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 activity, with the 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 customers’ software, they are shipping more code faster across a growing mix of models, agents, and infrastructure. Of course, this generates complexity that is very quickly outpacing what even the best engineering teams can track on their own. And that’s exactly the problem we’re 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 (Chief Financial Officer)
Thanks, Olivier, and good morning to everybody. Our Q3 revenue was $1.197 billion, up 35% year over year. We continue to see robust usage growth from existing customers as well as a strong ramp in our new customers. Revenue growth accelerated within our broad base of customers excluding AI customers to the high-20s percent year over year, up about 1 percentage point from last quarter and up from about 20% 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% in Q3, up from 25% in the year-ago quarter.
And now regarding our retention metrics, our trailing twelve-month net revenue retention percentage was in the low 120%, similar to last quarter, and churn remains low, with 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% year over year. Remaining performance obligations, or RPO, was $3.85 billion, up 38% 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 indication 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%. This compares to a gross margin of 79.6% last quarter and 81.2% in the year-ago quarter. Q3 opex grew 25% year over year versus 26% last quarter and 32% in the year-ago quarter. And Q3 operating income was $334 million, or 28% operating margin, compared to 23% last quarter and 23% 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% quarterly. The timing of collections and payments vary, and on a trailing twelve-month basis, free cash flow margin was 26%.
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 to $1.2 billion, which represents a 25% to 26% 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 to $290 million, which implies an operating margin of 24%. The fourth quarter includes large marketing investments, including AWS re:Invent.
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 to $4.525 billion, which represents approximately 32% year-over-year growth, and non-GAAP operating income is expected to be in the range of $1.095 to $1.105 billion, which implies an operating margin of 24%. Finally, non-GAAP net income per share is expected to be in the range of $2.73 to $2.75 per share, 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 to $40 million, and we continue to apply a 21% 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% 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 want to thank Datadogs worldwide for their efforts. Now, with that, we will open the call for questions. Operator, let’s begin the Q&A.
OPERATOR
Thank you. To ask a question, you will need to press *11 on your telephone and wait for your name to be announced. To withdraw your question, please press *11 again. Please stand by. We’ll compile the Q&A roster. One moment for our first question. Our first question will come from the line of Sanjeet Singh from Morgan Stanley. Your line is open.
Sanjit Singh (Analyst at Morgan Stanley)
Yeah. Thank you for taking the questions, and glad for the strong results on Q3. David, I want to start with the Q4 guide. If you wind back 90 days ago, has anything changed with respect to your underlying assumptions between the different cohorts, between the core business and the sort of largest customers versus 90 days ago? Or is this outlook sort of a reflection of that minimum commitment methodology that you’re applying in Q4, just to get a sense of has anything changed with respect to the outlook?
David Obstler (Chief Financial Officer)
Yeah, there are a couple. Thank you, Sanjit. There are a couple things to talk about. 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 were going to only include the minimum commit, despite the fact that in Q3 they were spending above the minimum commit. As we talked about, we’ve accelerated in Q3, excluding the largest customer.
So I would say the only thing that has changed was in Q3 we did some renewals, extensions with some large and large AI customers where, as normal, we essentially extend and increase the commitment but do better pricing for that. And that happened in a number of large customers. It’s our normal behavior. And that’s really the only thing that changed since our last communication. Ali, do you want to add anything?
Olivier Pomel (Co-Founder & CEO)
Yeah, there’s a few we discussed on the call. We bring up a few of those large customers where we saw renewals way earlier than we thought we would. There’s another one that happened in October that we’re not discussing. So these are all good news. But these do create headwind in the short term into the revenue, and that’s one change from last quarter. Other than that, everything else played out pretty much the way 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 (Analyst at Morgan Stanley)
Understood. And then if we think about the ex-AI native customer growth that’s been on an accelerating trend for several quarters now, as we think about this core non-AI native business, you obviously have a lot of tailwinds on the AI portfolio. It seems like 100k-customer cohorts are adopting more of the AI product. Do you feel that that can continue to drive a sustained trajectory, or are we, after multiple quarters of acceleration, seeing a leveling off in that ex-AI native cohort?
Thank you.
Olivier Pomel (Co-Founder & CEO)
I mean, look. All of these customers are adopting AI and they consume more cloud and they’re building more software and they’re changing the way they work. So we expect a long period of growth for these customers. Now, what the overall growth rate is—does it keep going up a lot, or does it stay stable? It’s a very high growth rate already and we feel pretty good about it. It’s hard to tell where it’s going to go, but we’re very confident in the broader trend that is underpinning that.
OPERATOR
All right, thank you. One moment for our next question. Next question will come from Gabriela Borges from Goldman Sachs. Your line is open.
Gabriela Borges (Analyst at Goldman Sachs)
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’ve been very transparent in how you talk about your largest 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’ve got this renewal dynamic with your largest customer. You’re also talking about the renewal dynamics on the early renewals.
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, two quarters and then we’re back to steady state? Is it that maybe we see another step function down every year in the sequential because you have this early renewal dynamic, which again is a really good place to be because you’re getting these really large deals that the only way you get better pricing is because the dollar volume is going up?
So maybe just the puts and takes to how we think about the sequentials for the AI native cohort and when we’ll be back to something that looks like normal, which I get is a tricky definition of the word normal anyway.
David Obstler (Chief Financial Officer)
Yeah, so good question. So in terms of the largest customer, I think as we said, the customer renewed. We decided to put only the commitment, the minimum commitment. So the customer in Q3 spent substantially above the minimum commitment. So one thing that is very important in the sequentials is what happens there. We don’t control that—the customer can’t go below the minimum commitment but can be various degrees above the minimum commitment. So that is one factor.
Essentially one thing that will create less of a sequential guidance or actual would be that that number stabilizes and isn’t running above the minimum commitment. So that’s one,
Olivier Pomel (Co-Founder & CEO)
I would say broaden it. I mean, the point here is one part that is very usual, which is that we have all these customers that are landing and they’re growing and they’re renewing, and then when they renew they get a better deal and they also adjust their traffic over time with an underpinning of growth, which is what we’ve seen since the beginning of the company. There’s something that is new: we have some of these companies that are scaling incredibly fast to a level that we’ve never seen in the industry—or any industry really—before, which creates new dynamics.
This is why we can be a bit surprised when we see customer renewals early just a few quarters into their starting to adopt the product. In general we’re getting a larger number of these customers. The AI native cohort is broadening. We have many more customers, many more large customers, into that cohort. We called out a few wins—we landed another AI lab, for example, this quarter that’s in the notes. So 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 don’t think it’s a very unstable business.
I mean, we’re talking about a chance of a few percentage points quarter to quarter in terms of where we end up versus where we think we’re going to be. But, you know, we think at the end of the day this is a usage-based model and we get more visibility into the mid to long term than we do into the very short term.
David Obstler (Chief Financial Officer)
I do want to remind everybody that, all things being equal, the sequential between Q3 and Q4 tends to be less because of the late December effect with our usage-based products, vacations, and some code freezes. So that can be transparent based on the other factors, or it might be underneath that. But we do have that effect in Q4, which we don’t have in the other quarters.
Gabriela Borges (Analyst at Goldman Sachs)
These are really helpful comments. Thank you.
David Obstler (Chief Financial Officer)
Yeah, thank you.
OPERATOR
Thank you. Our next question will come from Raimo Lenschow from Barclays. Your line is open.
Raimo Lenschow (Analyst at Barclays)
Thanks, thanks. Can I stay on one last time—one last question on this one. So if I think about the customers renewed early so they get better pricing, but obviously the volume is going kind of crazy there, it 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 the normal progression. Is that kind of the right thing to think about? I think we had that in the past, but I just wanted to clarify that.
And then one follow-up.
Olivier Pomel (Co-Founder & CEO)
Yeah, that’s partly what happens. There’s a few other things that typically happen in those situations, which is they try to understand what it is they’re going to need. So they look at what they’re using and they think, okay, this part maybe we don’t need as much of, this part we probably need more of. Or maybe in 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’re using it.
So there’s a bit of account optimization there when we work with them. But this is on the background of growth. These customers are growing and they’re deploying much larger workloads and they’re looking at doubling, tripling, decupling their footprint with us over time. And this is basically what we’re working on with them.
Raimo Lenschow (Analyst at Barclays)
Okay, perfect. And then the main question for me was on the ex-AI part. Historically we would think about that growth coming from cloud migration, people modernizing, doing more stuff. You mentioned another aspect, which is because of AI, we can code more, there’s more application modernization, all this stuff happening. So is this changing the way it’s evolving for you from, you know, classically with people modernizing, you have to go to Datadog, while now actually the companies are working differently and that’s also starting to drive the ex-AI business?
Can you speak to that, please?
Olivier Pomel (Co-Founder & CEO)
Yeah, I mean, well, there’s one thing I didn’t mention on the call, but that’s always true. We also do more things for our customers. You know, we consolidate 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, it also brings some of our other products more into the front and center. So an example is customers are building a lot more software, so they’re putting a lot more pressure on their CI and deployment and testing, and so they’re starting also to adopt more of our CI products.
I think we’ve called out a few wins around that too in the core. So that opens the door for more products beyond core observability for us.
Raimo Lenschow (Analyst at Barclays)
Yeah. Okay, perfect. Thank you. Well done.
OPERATOR
Thank you. Our next question will come from the line of Samik Chatterjee from JP Morgan. Your line is open.
Samik Chatterjee (Analyst at JPMorgan)
Hi. Thanks for taking my questions, and I had a couple. Maybe for the first one, I know you mentioned your largest customer did spend above their commit in Q3. Is there any way to tell us how much of the beat relative to your guidance in Q3 was because of the large customer spending above the commit relative to the rest of the business, depending on how you forecasted them in Q3? And I have a quick follow-up.
David Obstler (Chief Financial Officer)
Yeah, we’re not being specific, but it is true that if you’re basically guiding below their—We said we’d be conservative in Q3, we said conservative relative to what they were spending, and in Q4 we’ve been explicit and said we would use the minimum commitment. It is true that if they spend more than that, that would be implicit—mathematically, yeah.
Olivier Pomel (Co-Founder & CEO)
We didn’t change the assumption there over time. We kept waiting unchanged. I think the one difference we brought up earlier—the one difference since the last quarter—is some positive trends on the overall customer growth but also some early renewals of larger customers, which includes some other AI customers. That’s the one thing you should remember as being the difference.
Samik Chatterjee (Analyst at JPMorgan)
Yeah, sure. And somewhat of a follow-up. I mean, the contribution of revenue from new customers continues to ramp up steadily. Can you just talk about what is the average land size that you’re seeing now with new customers and how that’s changed, and what sort of behavior you’re seeing on that front? How are you thinking about it as you move into next year in terms of how much new customers contribute to your overall growth? Thank you.
David Obstler (Chief Financial Officer)
We haven’t provided guidance on that. We said over time we’ve 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. Ali, do you want to—
Olivier Pomel (Co-Founder & CEO)
Yeah, I think this is something we see. We see that one is with the new and the AI-native companies; some of them start with more scale earlier, like we discussed before. But, you know, we see companies scaling faster than they used to, so they have more of the revenue faster. But we also are developing new segments and landing large customers from day one. You know, we pulled out a few on the script today. We mentioned, I think, a defense agency in the U.S. government.
We mentioned Japanese banks, like two of the three largest Japanese banks in the same quarters. Those are all sizable land deals. And if you compare the business to what it was, say, five years ago, this is definitely a new, sizable business that we didn’t necessarily have before. And by the way, those wins I mentioned, they don’t even register in the revenue yet because these are lands, but these are not customers that are ramped users yet. Thank you.
OPERATOR
Thank you. Our next question comes in, Matt Hedberg from RBC. Your line is open.
Matt Hedberg (Analyst at RBC Capital Markets)
Great. Thanks for taking my questions, guys. Appreciate it. I wanted to start with training. You noted in your script you had an eight-figure, I think annualized, expansion with an AI division of a large software company. Sounds like they’re using you guys for AI training workloads. I guess this, to us, 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 (Co-Founder & CEO)
I mean, it’s been evolving positively. If you look a year or two years ago, basically nobody was doing training. It was only a few frontier labs doing their own training and nobody else was. Now we see an order of magnitude more large AI labs, or new labs, that are doing training at a large scale. And then there’s another whole category of companies below that that are starting to do their own, including us. We started doing training at a pretty decent scale, and we showed some of the results on the call as well.
So I think there’s some interesting potential development there where training becomes a part of everybody’s business and everybody’s customizing models and building their own. And so we’re investing. I would say it’s early to call it a mass market. 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, we still have to build, we still have to see how that market develops.
Matt Hedberg (Analyst at RBC Capital Markets)
Got it. And actually, David, if I could just have one more follow-up. You know, on our largest customer, you used the word “substantially above their minimums,” which I find interesting. And I know you’re assuming only the minimums for 4Q, 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’re using today — that could make that growth opportunity really interesting for this customer?
David Obstler (Chief Financial Officer)
I think that’s true for any customer, that there are many different use cases for Datadog. In particular, as we said all along, for many quarters, we don’t control — we have the contract, the renewal — and we’re not in the business of projecting that. But it is true across our customer base that there are many use cases in a company, whether it be division and end user, et cetera. Ali, you win.
OPERATOR
All right, thank you. Our next question will come from the line of Carl Kirstead from UBS. Your line is open.
Carl Kirstead (Analyst at UBS)
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’m just wondering, based on all your customer interactions, how enterprise spending behavior is changing in light of the fact that they’re 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’re trying to grapple with budget pressure?
Thanks so much.
Olivier Pomel (Co-Founder & CEO)
Yeah, so I think, look, this is a very widespread problem. What we’ve seen is there’s been an explosion of adoption of AI, in particular coding agents. And that was not planned. Largely, a year ago nobody had the amount they spend in their budget on AI, so they really had to try and figure out how that works and what part of that they actually need and what they get from that. So there’s a strong need for tooling, which is something we provide. It’s a great opportunity for us, and we see great growth and adoption of our Cloud Cost Management products.
Now, in terms of the early renewals that we’ve seen, they’re not directly related to that. I think they come from large customers that are either AI labs or that are focused on — that are tech companies in general, I would say. And that doesn’t really have anything to do with their own token consumption. It has more to do with their own growth as a business.
Carl Kirstead (Analyst at UBS)
Okay, thank you.
OPERATOR
Question will come from the line of Fatima Bulani from Citi. Your line is open.
Joel (Analyst at Citi)
Hi, good morning everyone. This is Joel on for Fatima. I’ll maybe ask two real quick. So firstly, just on margins and operating gross income leverage, maybe for you, David — strong results this quarter, at least relative to our expectations — but we do know there’s 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 your updated, or what your latest, investment posture is.
And then just the quick second question, I’ll just throw it in now, is any data points on Bits AI penetration specifically in the question customer base. Thank you.
David Obstler (Chief Financial Officer)
Do you want to— Yeah, I’ll go margin first. Yeah, yeah. It’s 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 you see that in the addition. We’re continuing to invest in R&D. In this case it’s a combination of investments in AI and people. We see lots of opportunities. I think Ali will now go talk about the products.
Want to go for the Bits AI?
Olivier Pomel (Co-Founder & CEO)
Yeah. And on this, I mean we’ve mentioned on the call we see amazing adoption of it. When we look at the number of individual customers or users that are using it, this is the strongest launch we’ve ever had as a product. So we feel very good about it. We’ve also revamped the way we package it over the past quarter. 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 AI as our agent, and we’ve seen great results thanks to that.
So it’s super encouraging. I would say still early because there’s still so much that we’re building into Bits AI — so many things our customers want us to do for them, so many opportunities to automate their workflows. But we feel very, very good about the current status.
David Obstler (Chief Financial Officer)
Let me just circle back on one thing. I think I neglected to go over gross margins you mentioned. I think it’s very similar to what we said before: a combination of the penetration of our platform as well as our engineering. We have been 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 what was communicated in gross margin indicates stability there, which we’ve evidenced in previous quarters.
OPERATOR
Thank you. One moment for our next question. Our next question will come from the line of Kirk Materne from Evercore ISI. Your line is open.
Kirk Materne (Analyst at Evercore ISI)
Yeah, thanks very much for taking the question. I guess, David or Ali, on this native AI cohort that’s renewing a little bit early — obviously they’re coming back because they’re spending so much with you all and want to get better economics. I’m just curious, what’s the attach rate on some of those newer renewals? When they come back and renew early, do you still have time to cross-sell them some of the other products? 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 expanding their usage across multiple products. Thanks.
Olivier Pomel (Co-Founder & CEO)
Yeah, I mean, look, if you look at these cohorts, and if you compare them in particular to the planet cohorts we might have had five or six years ago, these are adopting many more products. So we’ve seen much broader product adoption and platform adoption with those customers than we saw five, six years ago. So that’s definitely something that is happening. Some of that is happening at renewal time; sometimes it’s just happening organically as new needs emerge and always spread to more teams inside those companies.
David Obstler (Chief Financial Officer)
But I think it’s important to clarify these are renewals, but they are at higher levels than their previous commit, which is how they’re getting the volume discounts. But we are renewing these customers at higher commits.
Kirk Materne (Analyst at Evercore ISI)
Okay. And David, I guess just for you, this cohort coming up for renewal earlier — it’s all good over the long term, I’d imagine; just visibility is a little bit harder from a sequential perspective. Is that sort of to Ali’s comments? On the long term, I’d imagine it’s all good, but it just creates a little bit more volatility for you on a quarter-out guidance perspective. Is that fair?
David Obstler (Chief Financial Officer)
I would say it has a small amount of variation in the timing of the revenue path of these customers. The sequential here was really three things. It was, one, that factor. Two, I think we mentioned the largest customer in the guide, which, you know, we’re not predicting what they’re going to do. And three is we know about the small amount of seasonality. So it’s one of the factors — we try to get a good sense of that. But of course, as our RPO and as our contracts extend, it does create some volatility around the edges in the sequential nature.
Olivier Pomel (Co-Founder & CEO)
Again, it’s all good news. Look, we are using an edge-based model, and when we deal with fast-growth customers, these customers themselves don’t know exactly how they are going to grow over the next few months and what part of their business and how their consumption is going to change. So we happily work with them and make sure that we support their growth. That’s right. The only time we think about it in difficult terms is when we prepare for these calls.
We have to figure out, okay, what part can we communicate, what part do we know, what part do we not know? But overall, these are all great news: great customers, great growth, great adoption, amazing trends that are underpinning the industry as a whole. So we feel very good about it.
David Obstler (Chief Financial Officer)
And this is similar to what we’ve done in the company’s existence that you talked about. Our motion is to increase the commitment of customers, broaden the platform, and extend the contract. So that’s what we do all the time. It has some variability but is very similar to what we’ve been doing in the company throughout its history. And it’s a good thing.
Kirk Materne (Analyst at Evercore ISI)
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 (Analyst at Bernstein Research)
Thank you so much. We talked about it a little bit, but it’s 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 (Co-Founder & CEO)
Yeah, so it’s actually a bit of both. You know, so look, it largely sits on all of the data we acquire and the way we can process it and all the various products we have. You know, so that’s the core. But we also have a model where we charge for AI creative specifically on top of that. And the model we’ve chosen is a model where we stay fairly close to inference costs there so that we can pass on the savings as the models become more efficient to our customers and so we can run many more investigations.
And the reason we’ve done that is the field is changing so fast and we’re improving the efficiency of those models so fast that, you know, it’s not very productive to have a fixed price on a strategic thing you do as you expect the volume of things you do to increase by orders of magnitude while the price goes down by orders of magnitude as well. And so we’ve already done that model for that which works really well. So long term we expect both to monetize Bits directly as we do today, and to drive broader adoption of the platform and deliver more value to our customers.
In terms of what becomes the dominant monetization, it’s hard to tell and we don’t really care. Again, we’re fully usage-based, so as long as we get more usage, deliver more value, we’ll be fine.
OPERATOR
Thank you. Thank you. Our next question comes to the line of Koji Ikeda from Bank of America, your line is open.
Koji Ikeda (Analyst at Bank of America)
Yeah. Hey, thanks, guys. I wanted to ask a question on infrastructure monitoring. So you guys disclosed it’s 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 (Co-Founder & CEO)
So, I mean, look, we’ve seen acceleration of the business in general and not just thanks to AI natives, but also thanks to the rest of the customer base. So we mentioned that and that applies to infrastructure monitoring also. Today that growth is not actually related directly to GPUs. The GPUs are still largely on, we see them for customers, on training. We have products there, but we don’t monetize those products widely yet. And so for us, that’s an opportunity we have in front of us.
That’s not something that’s baked into the current growth, the current numbers.
OPERATOR
Thank you. Our next question will come from the line of Patrick Covo from Scotiabank. Your line is open.
Patrick Covo (Analyst at Scotiabank)
Thank you so much for having me on. To me, one of the most interesting points in the prepared remarks was about Datadog training your own frontier models. And then Olivier, your comments are this behavior is not mass market, but seeing how the market develops. I guess I want to ask you, because you’re at the forefront of how things are changing, how does this affect Datadog’s use of proprietary models internally or for customers versus open-weight models?
And then I guess, David, if this shift that Olivier is describing picks up steam, what might that mean for Datadog profitability that nicely increased this quarter? But is there potential that this might help the bottom line as well? Thank you.
Olivier Pomel (Co-Founder & CEO)
Look, what we see internally and what we see with customers is there’s a mix of models that are being used. So I think I can say we and include both customers and us into that collective we because the behaviors are, you know, fairly similar. We’re 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 our own and post-train them, and I think it’s 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. And there’s 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’s finally a number of other things that you can make way more efficient with your own data, your own workflow, and your own post-training, which is what we’ve done with some of the models we’ve announced.
So I expect that mix to remain about the same. In terms of the impact on margin, look, at the end of the day you get some control on what you spend your money on. If it’s very proprietary to you and you can deliver superior economics, you should absolutely do so. If not, like if it’s very undifferentiated and applies broadly in some parts of your business that understand to millions of other businesses, then it’s fine probably to use an off-the-shelf model and you can keep it.
OPERATOR
Thank you. Our next question will come from the line of Alex Zukin from Wolfe Research. Your line is open.
Alex Zukin (Analyst at Wolfe Research)
Hey guys, thanks for getting me in. Ali, maybe a quick one for you and Dave, a follow-up. Just to what extent are you seeing the demand for agent observability for both AI natives and the core driving demand for offerings like your traces product? And what has changed here, longer term, given the kind of elevated adoption levels? And maybe also any comments on cybersecurity and the plans there?
Olivier Pomel (Co-Founder & CEO)
Yeah, so we see growing demand for agent observability, and so there was always interesting demand from the AI, et cetera. But what’s most interesting here is the non-AI natives are continuing more and more, and because they’re getting deeper into AI, we see real demand, real volumes, and really exciting pipeline for these products actually. So we think that this is crossing the chasm, so to speak, and that’s exciting for this product, even though we didn’t mention any hard numbers today on that.
On cybersecurity, look, there are so many new opportunities with AI. We’re very happy to see that we can land the full platform with large customers. We mentioned that for a defense agency. We mentioned that also for, I think, a couple 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.
Alex Zukin (Analyst at Wolfe Research)
Perfect. And then, David, maybe for you, just a clarification. First of all, it’s great to hear about the renewal with your largest customer. I think we totally understand the guidance for the minimum for Q4. Any pearls of wisdom on how to think about modeling that for 2027? Because presumably that’s a one-year contract. And is that something we should think about kind of maintaining at some level? Is that something that you guys are going to take out of the model?
And then if I think about how you’re guiding with respect to some of those other early renewals for the AI natives that renewed, are those also at the minimum commitment level? Are those slightly above? Just any help on those two items I think would be helpful.
David Obstler (Chief Financial Officer)
Yeah. Reminding everybody—yeah, yeah, sorry. Yeah. We’ll give guidance for 2027 on our year-end call. You know, I think we’ve said in the last couple quarters that we’re guiding at whatever the contractual minimums and, you know, we’ve repeated that this quarter. We’ll give some more flavor on that when we do 2027. And in terms of the rest of the business, what we do is, and we’ve said this over 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 (Co-Founder & CEO)
Thank you, guys. All right, and I think that’s 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. All very, very busy. We’re meeting—I’m going to meet a lot of our customers at the largest AWS conference in a few weeks, and we hope to have many more exciting product announcements 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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