Datadog (DDOG.US) Q3 Earnings Call: Traditional Customer Growth Rises for Fifth Consecutive Quarter; AI Agents Reshape Software Operations; Major AI Application Customer Renews Early

Title context: Datadog (DDOG.US) Q3 Earnings Call: Traditional Customer Growth Rises for Fifth Consecutive Quarter; AI Agents Reshape Software Operations; Major AI Application Customer Renews Early Text: The primary incremental takeaway from the cloud monitoring and analytics software company Datadog's (DDOG.US) Q3 2026 earnings call is arguably that growth momentum is spreading from a handful of large AI customers to a broader base of enterprise customers. Non-AI customer revenue growth accelerated for the fifth consecutive quarter, indicating that demand driven by AI comes not only from model companies' own expansion but also from traditional enterprises using AI to write more software, accelerate application modernization, and consequently increase demand for continuous integration, testing, deployment, and observability tools. At the same time, integration of the Datadog software ecosystem platform is increasing the initial contract size of new customers: customers such as a U.S. federal defense agency and major Japanese banks adopted multiple products from the outset of the relationship, and management explicitly noted that these new customers have not yet fully ramped usage, with their revenue contribution still to be unlocked. The analyst Q&A session further clarified in detail the three factors behind the Q4 guidance: large customers receiving more favorable unit pricing after early renewals, the largest customer being counted in guidance only at its minimum consumption commitment, and seasonal effects from year-end holidays and code freezes. Early renewals come with higher committed consumption and broader product adoption, with short-term unit price adjustments occurring alongside long-term customer expansion. Management also explicitly stated that these early renewals are mainly related to customers' own business growth and are not directly related to enterprises cutting spending to control token budgets. The largest customer's actual consumption in Q3 was significantly above its minimum commitment, but the company did not extrapolate its future consumption on that basis; other customers were conservatively discounted based on recent growth trends rather than uniformly counted at minimum commitments. Formal 2027 guidance will be provided at the year-end call. On products and business model, Bits AI has become the fastest-adopted product in the company's history, with billing shifting from charging separately per investigation task to AI credits covering multiple use cases. Management emphasized that pricing will remain close to inference cost, passing savings from model efficiency improvements to customers to support more investigations and broader platform usage. The in-house site reliability engineering model Scout, along with reinforcement learning and post-training capabilities, is driving the company from providing monitoring tools toward autonomous operations. Its commercial value includes both direct revenue from Bits itself and further adoption of products such as logs, metrics, traces, security, and cost management. Another key incremental point is that GPU monitoring and training observability remain in the early stages of commercialization. Management explicitly stated that current infrastructure monitoring growth is not directly driven by GPU monetization, and related products have not yet generated broad revenue; as more enterprises train, customize, and post-train models themselves, this could create new demand space. Meanwhile, actual demand for agent observability is spreading from AI-native companies to traditional enterprises, and AI cost management is benefiting from enterprises' need to track rapidly growing token spending, effectiveness, and return on investment. The company is thus participating in both growth paths: "using AI to manage software" and "managing AI itself." Below is the full transcript of Datadog's earnings call (translated with the assistance of AI tools): Conference Host Hello, everyone, and thank you for your patience. Welcome to Datadog's Q3 2026 earnings conference call. Currently, all participants are in listen-only mode. After the speakers' remarks, we will open the line for questions. To ask a question during the Q&A session, please press star and 11 on your telephone. You will then hear an automated voice prompt confirming that your hand has been raised. To withdraw your question, please press star and 11 again. Please note that today's conference call is being recorded. Now, I will turn the call over to the first speaker, AJ Lubitsch, Senior Vice President of Strategic Finance and Investor Relations. Please go ahead. AJ Lubitsch (Senior Vice President of Strategic Finance and Investor Relations) Thank you, Victor. Good morning, everyone, and thank you for joining us to review Datadog's Q3 2026 financial results. We released these results in a press release issued this morning. Joining me on the call today are Olivier Pomel, Datadog's co-founder and Chief Executive Officer, and David Obstler, Datadog's Chief Financial Officer. First, I would like to thank Yuka Broderick for her outstanding work leading investor relations over the past five and a half years; she is moving on to a new role within Datadog. I am excited to be back in the investor relations role. I worked with many of you during the company's 2019 initial public offering. During this call, we will make forward-looking statements, including statements regarding our future financial performance, our outlook for Q4 and the full year 2026 and related notes and assumptions, product capabilities, and our ability to capture market opportunities. "Expect," "believe," "continue," "estimate," "anticipate," "intend," "will," and similar expressions are intended to identify forward-looking statements or other similar future expectations. These statements reflect our views as of today and are subject to various 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 actual results, please refer to our Form 10-Q for the quarter ended June 30, 2026. Additional information will be included in our upcoming Form 10-Q for the fiscal quarter ended September 30, 2026, and other filings with the Securities and Exchange Commission. This information, along with a replay of this call, is also available on the investor relations section of our website. We will discuss non-GAAP financial measures. Reconciliations between these measures and the most comparable GAAP financial measures are included in the earnings press release and available at investors.datadoghq.com. With that, I will turn the call over to Olivier. Olivier Pomel (Co-Founder and Chief Executive Officer) Thank you, AJ, and thank you all for joining us today to review our third quarter results. I will start with the business drivers in the quarter. I am pleased to report that we delivered strong business performance in Q3. Business was strong across the board, including across our product lines and our diversified customer base. Revenue growth from non-AI customers accelerated for the fifth consecutive quarter, at the high end of the 20% range year over year, up about 1 percentage point from last quarter and above the roughly 20% level in the year-ago period. In addition, our AI customer cohort remains a strong growth DRIVE. This cohort continues to grow and become more diversified, both in the number of customers we serve and the size of those customers. Overall, we continue to see healthy demand. From the most technically forward-looking startups to the largest enterprises in the world, customers across the board are building with AI and modern technology. We believe this is driving further adoption of the cloud and the Datadog platform. On Q3 financial performance and key metrics, our revenue was $1.2 billion, up 35% year over year, above the high end of our guidance range. We ended Q3 with approximately 33,500 customers, up from approximately 32,000 a year ago. Customers with annual recurring revenue of $100,000 or more totaled approximately 4,860, up from approximately 4,060 a year ago. These customers contributed approximately 91% of our annual recurring revenue. We generated $251 million in free cash flow, a free cash flow margin of 21%. Turning to product adoption, our platform strategy continues to gain market recognition. Currently, 61% of customers use four or more products, up from 54% a year ago; 41% of customers use six or more products, up from 31% a year ago; and 16% of customers use ten or more products, up from 8% a year ago. So we are growing our customer count and delivering value across more products through a unified platform. One milestone we are very proud of: the annual recurring revenue of our infrastructure monitoring product suite has now exceeded $2 billion, growing at the mid-30% range year over year. Infrastructure monitoring was the first product we brought to market more than a decade ago. Today, customers rely on it for a complete view of their environments, including cloud infrastructure, on-premises servers, containers, serverless applications, and GPU clusters. While reaching this milestone is very exciting, we still feel we are just getting started. According to industry analysts, we rank first in global observability market share, but our share remains in the teens. Turning to R&D. As AI changes how customers build and run software, we have been investing and advancing on two fronts: empowering Datadog with AI and serving AI with Datadog. First, empowering Datadog with AI. These AI products and features make the Datadog platform more useful and increasingly automated for customers. First, our flagship agent Bits AI is off to a very strong start. Since its launch, it has become the fastest-adopted product in our history. In October, Bits Testing became generally available. With a single click, it can discover an application's key user flows and generate tests covering those flows. Meanwhile, our AI research team is pushing the boundaries of Bits and the platform. Last month, we launched Scout, our in-house reasoning model purpose-built and optimized for site reliability engineering tasks. Scout was built using our internal distributed reinforcement learning infrastructure and currently powers Bits investigations. This is exciting for two reasons. First, on alert investigation, it performs on par with or better than foundation models at a fraction of the inference cost. This is valuable because it allows Bits to investigate more alerts, engage earlier, and conduct deeper investigations into incidents, becoming the customer's always-on first responder. Second, it demonstrates what can be achieved by training models on our own production data, key user workflows, and internal tools. Scout is just the beginning. With the Adaptive ML team that joined us in June, we are accelerating our work on reinforcement learning and model post-training across the board. Over time, we expect this research to drive step-function improvements in capabilities across the platform, bringing us closer to autonomous operations. Next, serving AI with Datadog. These products provide end-to-end observability and security for the new AI stack. As AI moves from experimentation to production, customers now need to answer three key questions: How is it performing? Is it secure? What is the return on investment, or is the spend worth it? And because AI does not run in isolation, they want these answers on the same platform where they manage the rest of their stack. On performance, we cover the entire AI stack, from compute resources all the way to agents. GPU monitoring now goes beyond hardware reliability, with new capabilities that help customers maximize cluster utilization, improve training throughput, and reduce inference serving cost per request. Year to date, the number of GPU devices we monitor has grown eightfold since the start of the year, and some frontier AI lab customers are co-developing these capabilities with us as design partners. Further up the stack, our agent observability product helps teams monitor and evaluate agents in production, including response quality, latency, and token usage. Year to date, the number of trace spans sent to agent observability has now grown ninefold since the start of the year. We have also made agent observability available on the Anthropic app marketplace, and we are a launch partner for OpenAI's new enterprise app marketplace. So customers can now purchase our products directly through the frontier AI labs they already work with. On security, our AI Guard product can discover custom agents running within an organization and, leveraging the same real-time data collected to monitor those agents, protect them at runtime from prompt injection and data exfiltration threats. On cost, enterprises everywhere are grappling with rapidly growing token and AI spending. Beyond the three major cloud providers, AI providers are now the largest spend category customers monitor in our cost management product, or CCM. Now, with AI cost capabilities in CCM, customers can understand which teams, models, and agents are generating AI spend and manage that spend with budgets, forecasts, and anomaly alerts. Currently, more than one-third of CCM customers are tracking AI costs. The number of such customers has grown more than 11x year over year. Further up the stack, our agent console, building on CCM data, shows exactly which agents are actually being used, by whom, what tasks they perform, how much they cost, and most importantly, the associated outcomes. As we continue to innovate around empowering Datadog with AI and serving AI with Datadog, independent research firms are taking notice. Forrester named Datadog a Leader in its 2026 AIOps Platforms evaluation, and Gartner, in its August 2026 AI vendor race report, called Datadog the company others in the observability platform space need to catch up to. We believe this reflects our leadership in observability and our ability to help customers manage complexity in the AI era. Turning to sales and marketing, let's look at a few deals our go-to-market team closed in what was a very strong quarter. First, we signed a seven-figure annualized expansion contract with a leading software company. This customer more than doubled its annual commitment, is putting AI agents into production, and will replace a standalone commercial tool with Datadog's agent observability product to monitor cost and quality. By using Flex Logs, they can also ingest and correlate larger volumes of logs at lower cost. In total, they will consolidate three tools onto Datadog and increase their Datadog product count to 20, including our on-call management product On-Call and Bits AI. Next, we signed a five-year, seven-figure annualized contract with a U.S. federal defense agency. This customer runs thousands of cloud accounts across hundreds of mission owners. As it modernizes, manual-dependent tools left it 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 the public sector team on this milestone customer win. This is a direct result of our investment in the government space and the high-level FedRAMP certification we obtained earlier this year. We are also actively expanding our platform for government to support workloads up to Impact Level 5, or IL5. This is the highest certification level for non-classified data and part of our long-term investment in the U.S. public sector. Next, we signed a seven-figure annualized contract with a leading home healthcare software company. As this customer grew, fragmented tools slowed incident response and hurt customer experience. They will consolidate four tools onto Datadog, replacing their existing logging and security vendors with Flex Logs and our cloud security information and event management product, and rely on our sensitive data scanner to help them maintain HIPAA compliance. This customer will start with nine Datadog products, including incident management and Bits AI, to resolve issues faster. Next, we signed a seven-figure annualized expansion contract with a leading online entertainment company. This customer saw a traffic surge during peak season and scaled effectively with Datadog. Their own AI agents already use a Model Context Protocol server, and they will now add Bits AI. This customer's Datadog product count will increase to 23, including agent observability and cloud cost management. Next, we signed seven-figure annualized contracts with two of Japan's most important banks, both of which are modernizing their core financial systems. Both banks chose Datadog as their unified standard to gain a unified view of cloud and on-premises environments. Both customers are adopting the full-stack platform, covering all three pillars as well as digital experience, security, software delivery, service management, and Bits AI, and are moving toward AI-driven operations. Next, we signed a six-figure annualized contract with a top European AI lab. This customer's developers were spending valuable time on slow builds and flaky tests, causing delays in a highly competitive market. They chose Datadog's continuous integration visibility and test optimization products, replacing a commercial tool, so their engineers can focus on shipping product. Their engineers also use Bits AI to reliably build monitors and custom analytics. Next, we signed a seven-figure annualized incremental expansion contract with a leading AI coding company, bringing the total annualized contract value to eight figures. This customer is growing rapidly and renewed early to keep pace with its expanding business. In this expansion, they will add Flex Logs and our new unlimited cardinality metrics capability to effectively control costs as data volumes grow. They are also one of the largest users of our Model Context Protocol server. This customer's Datadog product count will increase to 13. Finally, we signed an eight-figure annualized expansion contract with the AI division of one of the world's largest technology companies. Just two quarters after initially signing this customer, their annual commitment has increased to nearly three times the original. They are expanding their use of Datadog across large-scale AI training workloads. They are also adopting application performance monitoring and Bits AI to further reduce friction in engineering work. Alongside this customer progress, at the other end of the customer size spectrum, we are also increasing our investment in the many AI-native companies emerging every day. Through investment in the Datadog startup program, we are expanding its reach to a large number of earlier-stage startups and engaging in deeper engineering collaboration with those experiencing explosive growth. This is truly an exciting time to be working with startups. Now, before I turn the call over to David to review the financials, I want to say a few words about the long-term outlook. Our overall view is unchanged: digital transformation and cloud migration are long-term growth DRIVEs for our business. Today, AI is also another major growth DRIVE for us, because as AI workloads move into production, they drive more cloud resource consumption and more usage of the data platform. Today, we are already seeing this across our business: all of the top 15 AI leaders are our customers. More broadly, we now serve more than 850 AI customers, up from about 750 last quarter. Across our broader customer base, we are also seeing significant growth in AI activity. Currently, about one-third of customers use one or more of our AI products, including three-quarters of customers with annual recurring revenue of $100,000 or more. Many of these customers are just beginning to use these products, so there is still significant expansion opportunity ahead. We continue to see rapid growth in related activity, with Model Context Protocol tool calls up 50x year to date since the start of the year. We could not be more excited about this direction. As AI writes and runs more of our customers' software, customers are shipping more code faster on top of an expanding portfolio of models, agents, and infrastructure. Of course, the complexity this creates is rapidly outpacing even the best engineering teams' ability to track and understand it on their own. This is exactly the problem we are investing to solve. Empowering Datadog with AI to take on more of this work for them; serving AI with Datadog to help them run AI with confidence; and our AI research to turn data into models and, over time, achieve greater autonomy. That is why we believe more than ever that we are ideally positioned to help customers of all sizes and industries, and all types of users, whether human or AI agents, transform, innovate, and create value through the adoption of AI and cloud. With that, I will turn the call over to our CFO, David. David Obstler (Chief Financial Officer) Thank you, Olivier, and good morning, everyone. Our Q3 revenue was $1.197 billion, up 35% year over year. We continue to see strong usage growth from existing customers, along with rapid ramp of new customer usage. Across our broad non-AI customer base, revenue growth accelerated to the high end of the 20% range year over year, up about 1 percentage point from last quarter and above the roughly 20% level in the year-ago period. We saw strong growth across customer cohorts, spend tiers, and industries. Excluding our largest customer, revenue growth accelerated again. As we communicated in August, our largest customer reduced usage in Q3 but remained above its minimum commitment level. At the same time, AI customer growth continued to significantly outpace the rest of the business. This cohort of more than 850 customers continues to expand and diversify, including 39 customers who pay us more than $1 million per year. We continue to land larger new customers and grow faster, with new customers contributing an increasing share of revenue growth. In Q3, new customers accounted for approximately 30% of our year-over-year revenue increase, up from 25% in the year-ago period. Now on retention metrics. Net revenue retention over the trailing twelve months was slightly above 120%, similar to last quarter; churn remained low, with gross revenue retention stable in the mid-to-high 90% range. We believe this metric underscores the importance of our platform to customers' critical operations. Next, financial results. Billings for the quarter were $1.16 billion, up 30% year over year. Remaining performance obligations were $3.85 billion, up 38% year over year; current remaining performance obligations grew at the mid-30% range year over year. The duration of remaining performance obligations lengthened slightly compared with the year-ago period. As a reminder, we continue to believe revenue is a better reflection of business trends than billings or remaining performance obligations. Let's review some key P&L results. Unless otherwise noted, all metrics are on a non-GAAP basis. We have provided reconciliations of GAAP to non-GAAP financial data in the earnings press release. Q3 gross profit was $972 million, a gross margin of 81.2%, compared with 79.6% last quarter and 81.2% in the year-ago period. Q3 operating expenses grew 25% year over year, compared with 26% last quarter and 32% in the year-ago period. Q3 operating income was $334 million, an operating margin of 28%, compared with 23% in both last quarter and the year-ago period. Now to the balance sheet and cash flow statement. We ended the quarter with $5.2 billion in cash, cash equivalents, and marketable securities. Operating cash flow for the quarter was $290 million. After capital expenditures and capitalized software, free cash flow was $251 million, a quarterly free cash flow margin of 21%. The timing of collections and payments can vary; on a trailing twelve-month basis, free cash flow margin was 26%. Next, our outlook for Q4 and fiscal year 2026. First, our overall guidance philosophy remains unchanged. As a reminder, we base guidance on trends observed over recent months and apply prudent assumptions to those growth trends. With respect to our largest customer, our Q4 guidance counts revenue at its minimum commitment amount. For Q4, we expect revenue between $1.19 billion and $1.2 billion, representing 25% to 26% year-over-year growth. Excluding our largest customer, the revenue growth implied by our guidance for Q4 is in the low-to-mid 30% range. We expect non-GAAP operating income between $280 million and $290 million, corresponding to a 24% operating margin. Q4 includes significant marketing investment, including AWS re:Invent. Based on approximately 379 million weighted average diluted shares, we expect non-GAAP net income per share between $0.69 and $0.71. For fiscal year 2026, we expect revenue between $4.515 billion and $4.525 billion, up approximately 32% year over year; we expect non-GAAP operating income between $1.095 billion and $1.105 billion, corresponding to a 24% operating margin. Finally, based on approximately 375 million weighted average diluted shares, we expect non-GAAP net income per share between $2.73 and $2.75. A few additional notes on guidance: we expect net interest and other income of approximately $200 million for fiscal year 2026. We expect cash taxes of approximately $30 million to $40 million, and we will continue to apply a 21% non-GAAP tax rate for 2026 and beyond. Finally, we expect capital expenditures and capitalized software combined to be 3% to 4% of revenue for fiscal year 2026. Overall, we are pleased with our Q3 execution, delivering a combination of strong growth and strong profitability. We continue to invest to capture the long-term opportunities in cloud and AI. I want to thank Datadog colleagues around the world for their efforts. With that, we will open the line for questions. Operator, please begin the Q&A session. Below is the Q&A session of the earnings call (AI-assisted summary and translation): Conference Host Thank you. To ask a question, please press star and 11 on your telephone and wait for us to announce your name. To withdraw your question, please press star and 11 again. Please hold while we compile the Q&A roster. Please stand by while we connect the first question. The first question comes from Sanjeet Singh of Morgan Stanley. Your line is open. Sanjit Singh (Morgan Stanley Analyst) Okay, thanks for taking the questions, and great to see the strong Q3 results. David, I wanted to start on the Q4 guidance. If we go back 90 days, compared with then, have your underlying assumptions for different customer cohorts, including the core business and the largest customer, changed in any way? Or does this outlook simply reflect the minimum commitment methodology you are using in Q4? I am just trying to understand whether your view of the outlook has changed. David Obstler (Chief Financial Officer) Yes, a few things. Thank you, Sanjit. There are a few things to discuss. First, I think we said the largest customer had renewed, with spending above its minimum commitment. That was indeed the case in Q3. We said that although their Q3 spending was above the minimum commitment, we would only count the minimum commitment in guidance. As we discussed, excluding the largest customer, our growth accelerated in Q3. So I would say the only change is that in Q3 we completed renewals and contract extensions with some large customers and large AI customers. As usual, we essentially extended terms, increased commitment amounts, and provided more favorable pricing. This happened with several large customers. This is our normal practice. Since our last communication, that is really the only thing that has changed. Ali, anything to add? Olivier Pomel (Co-Founder and Chief Executive Officer) Yes, we discussed several of them on the call. We mentioned some large customers whose renewals came much earlier than we had originally expected. There was another renewal in October that we did not discuss here. So these are all good news. But they do create headwinds to near-term revenue, and that is a change from last quarter. Beyond that, things largely developed as we expected, with some pleasant surprises as well, especially the continued acceleration in non-AI customer growth, which is very good overall. Sanjit Singh (Morgan Stanley Analyst) Understood. If we consider growth excluding AI-native customers, that growth rate has been accelerating for several quarters. For this core non-AI-native business, your AI product portfolio clearly has a lot of tailwinds. It looks like the cohort of customers with annual recurring revenue of $100,000 is adopting more AI products. Do you think this can continue to drive sustained growth? Or, after multiple quarters of acceleration, is growth excluding AI-native customers leveling off? Thank you. Olivier Pomel (Co-Founder and Chief Executive Officer) I mean, look, these customers are adopting AI, consuming more cloud resources, building more software, and changing how they work. So we expect these customers to experience a long growth phase. As for how overall growth will trendwhether it will continue to rise significantly or remain stablethe current growth rate is already high, and we feel quite good about it. It is hard to say where it will go from here, but we are very confident in the underlying trends supporting this growth. Conference Host Okay, thank you. Please stand by while we connect the next question. The next question comes from Gabriela Borges of Goldman Sachs Group, Inc. Your line is open. Gabriela Borges (Goldman Sachs Group, Inc. Analyst) Hi, good morning. Thanks for taking the question. David, I wanted to follow up on the AI-native customer cohort, because I think you have been very transparent in discussing the largest customer versus the rest of that cohort. What I want to ask is, you now have somewhat irregular sequential performance, because over the past twelve months this cohort delivered very impressive growth, and now the largest customer has renewal-related changes. You also discussed early renewal dynamics. What I want to ask is, what framework can you provide for modeling the sequential performance of this business? Is it that after roughly the next two quarters we return to a steady state? Or, because of early renewals, might we see another step-down in sequential performance every year? Of course, that itself is a good situation, because you are signing these very large contracts, and customers get more favorable pricing precisely because contract amounts increase. So perhaps talk about what positive and negative factors we should consider in judging the sequential performance of the AI-native customer cohort, and when we return to something that looks "normal." I also know that "normal" itself is hard to define. David Obstler (Chief Financial Officer) Yes, that is a good question. Regarding the largest customer, I think as we said, this customer has renewed. We decided to count only the commitment amount, that is, the minimum commitment. So this customer's Q3 spending was significantly above the minimum commitment. Therefore, a very important factor affecting sequential performance is what happens on that front. We cannot control itcustomer spending cannot fall below the minimum commitment, but it can exceed the minimum commitment by varying amounts. So that is one factor. Basically, if that amount stabilizes and no longer exceeds the minimum commitment, it would make the sequential increase in guidance or actual results smaller. So that is the first point. Olivier Pomel (Co-Founder and Chief Executive Officer) I want to broaden the perspective. I mean, part of this is very common: we keep landing these customers, they keep growing and renewing; when they renew, they get more favorable terms and also adjust their traffic over time, while growth remains the foundation. This is what we have seen since the company was founded. What is new is that some of these companies are expanding at an astonishing rate, at a level we have never seen before in this industryor really in any industrywhich creates new dynamics. That is why we are sometimes surprised when we see customers renewing early just a few quarters after they began adopting the product. Overall, the number of such customers is increasing. The AI-native customer cohort is expanding. There are more customers in this group, and many more large customers. We named several customer winsfor example, we signed another AI lab this quarter, mentioned in our prepared remarks. So over the long term, this group will continue to expand and account for a much larger share of our customer base, so stability should increase over time. Still, I would not characterize this as a very volatile business. I mean, we are only talking about quarter-to-quarter differences of a few percentage points between the final outcome and our expectations. But you know, ultimately, we view this as a usage-based model, and we have more visibility over the medium and long term than over the very short term. David Obstler (Chief Financial Officer) I would remind everyone that, all else equal, sequential growth from Q3 to Q4 is typically lower because the late-December holidays and some code freezes affect our usage-based products. Depending on other factors, this effect may show up or may be masked. But it does exist in Q4 and not in other quarters. Gabriela Borges (Goldman Sachs Group, Inc. Analyst) That is very helpful. Thank you. David Obstler (Chief Financial Officer) Okay, thank you. Conference Host Thank you. The next question comes from Raimo Lenschow of Barclays. Your line is open. Raimo Lenschow (Barclays Analyst) Thank you, thank you. Can I ask one last question around this? My understanding is that customers renew early to get more favorable pricing, but their usage is clearly growing significantly, which creates a near-term headwind because the same usage now corresponds to a lower price; but then usage continues to grow, and after a few more quarters we should see normal growth trends. Is that the right way to think about it? I remember this happening in the past, and I just want to confirm. Then I have one follow-up. Olivier Pomel (Co-Founder and Chief Executive Officer) Yes, that is indeed part of it. Usually in these situations, something else also happens, which is that customers try to figure out what they will need. So they look at the services they are using and think, okay, we may not need as much of this part, and we may need more of that part. Or in some cases, we tell them that given how they use this type of data, perhaps they should adopt a different data retention tier. So when we work with customers, we also make some optimizations to their account usage configuration. But all of this happens against a backdrop of growth. These customers are growing, deploying much larger workloads, and want to expand their usage on our platform twofold, threefold, or even tenfold over time. That is basically what we are working through with them. Raimo Lenschow (Barclays Analyst) Okay, very clear. Then my main question is about the business excluding AI customers. In the past, we would think of this growth as coming from cloud migration, customers modernizing, and doing more business. You mentioned another aspect, which is that because of AI, we can write more code, application modernization increases, and all sorts of things are happening. So is this changing how your business develops? That is, in the past it was usually when customers modernized that they needed Datadog, whereas now it is actually changes in how enterprises work that are starting to drive non-AI business growth? Could you talk about that? Olivier Pomel (Co-Founder and Chief Executive Officer) Yes, I would say there is one thing I did not mention on the call, but it has always been there. That is, we are also doing more for customers. We have consolidated many of the tools customers used to use. We have more products and a broader reach. As customers build more with AI, some of our other products also become more important. For example, customers are building a lot more software, so the pressure on their continuous integration, deployment, and testing has increased significantly, and they are therefore also adopting our continuous integration products more. I think we also mentioned several customer wins in that area. So this opens the door for us to promote more products beyond core observability. Raimo Lenschow (Barclays Analyst) Yes. Okay, very clear. Thank you. Well done. Conference Host Thank you. The next question comes from Samik Chatterjee of JPMorgan. Your line is open. Samik Chatterjee (JPMorgan Analyst) Hello. Thanks for taking my question. I have two questions. First, I know you mentioned that the largest customer's Q3 spending did exceed its commitment amount. Can you tell us, relative to the Q3 guidance, how much of the excess came from this large customer spending above its commitment, and how much came from the rest of the business? Of course, that depends on how you forecasted this customer in Q3. I have a quick follow-up. David Obstler (Chief Financial Officer) Yes, we do not provide specific numbers, but it is indeed the case that if your guidance is basically below theirwe said we would take a conservative forecast in Q3, and we said our forecast was conservative relative to their spending level at the time. For Q4, we have clearly stated that we will base it on the minimum commitment amount. Indeed, if their spending is above that level, then naturally that meansmathematically, yes. Olivier Pomel (Co-Founder and Chief Executive Officer) We have not changed our assumptions on this over time. We have consistently kept our original assumptions unchanged. I think one difference we mentioned earlier, a difference compared with last quarter, is that there were some positive trends in overall customer growth, along with some large customers renewing early, including some other AI customers. That is the change you should keep in mind. Samik Chatterjee (JPMorgan Analyst) Okay, understood. One related follow-up. The revenue contribution from new customers continues to rise steadily. Can you talk about what the average initial contract size for new customers is now, how it has changed, and what behaviors you have observed? Looking ahead to next year, how do you see new customers contributing to overall growth? Thank you. David Obstler (Chief Financial Officer) We have not provided guidance on that. We said that over time we have seen average initial contract size increase, because the platform continues to expand and, as our disclosed product adoption metrics show, customers are adopting more parts of the platform. Ali, would you like to Olivier Pomel (Co-Founder and Chief Executive Officer) Yes, I think that is what we are observing. One thing we see is that among newly formed companies and AI-native companies, as discussed earlier, some are starting at a larger scale earlier. But we also see enterprises scaling faster than in the past, so they reach more revenue more quickly. But we are also opening new segments and signing large customers from day one. We listed several examples in our prepared remarks today. I believe we mentioned a U.S. government defense agency. We also mentioned Japanese banks, such as signing two of Japan's three largest banks in the same quarter. These initial contract sizes are quite substantial. If you compare the business now with five years ago, this is clearly a sizable new business that we may not have had in the past. By the way, the signings I just mentioned are not even reflected in revenue yet, because they have only just become customers and usage has not yet ramped. Thank you. Conference Host Thank you. The next question comes from Matt Hedberg of Royal Bank of Canada. Your line is open. Matt Hedberg (Royal Bank of Canada Capital Markets Analyst) Okay. Thank you all for taking my questions, much appreciated. I wanted to start with training. You mentioned in your prepared remarks an upsell deal with the AI division of a large software company, which I believe was an eight-figure annualized amount. It sounds like they are using you for AI training workloads. To us, this seems like a very noteworthy growth opportunity. Can you talk more broadly about what the opportunity is in training and what it could mean for your growth? Olivier Pomel