Good day, and thank you for standing by. Welcome to the third quarter 2025 Datadog earnings conference call. At this time, all participants are in listen-only mode. After the speaker's presentation, there will be a question-and-answer session. To ask a question during this session, you will need to press star one one on your telephone. You will then hear an automated message inviting you that your hand is raised. To withdraw your question, please press star one one again. Please be advised that today's conference is being recorded. I would like to hand the conference over to your first speaker today, Yuka Broderick, Senior Vice President of Investor Relations. Please go ahead. Thank you, Marvin. Good morning, and thank you for joining us to review Datadog's third quarter 2025 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. During this call, we will make forward-looking statements, including statements related to our future financial performance, our outlook for the fourth quarter and the fiscal year 2025, and related notes and assumptions, our gross margins and operating margins, 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 only as of today and are subject to a variety of risks and uncertainties that could cause actual results to differ materially. For 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, 2025. Additional information will be made available in our upcoming Form 10-Q for the fiscal quarter ended September 30, 2025, 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 their most directly comparable GAAP financial measures, in the tables in our earnings release, which is available at investors.datadoghq.com. With that, I'd like to turn the call over to Olivier. Thanks, Yuka. Thank all of you for joining us this morning to go through our results for Q3. Let me begin with this quarter's business drivers. We have seen broad-based positive trends in the demand environment, with an ongoing strength of cloud migration and digital transformation. Against this backdrop, we executed on a very strong Q3, both in new logo bookings and usage growth of existing customers. As a notable inflection, we saw acceleration of year-over-year revenue growth across our non-AI customers. The sequential usage growth for non-AI existing customers was the highest we have seen going back 12 quarters. This growth was broad-based as our customers are adopting more products and getting more value from the Datadog platform. We also experienced strong revenue growth for our AI-native customers and a broadening contribution to growth among those customers. There, too, we saw an acceleration of growth in our AI cohort in Q3 when excluding our largest customer. Looking at new business, contribution from new customers increased in Q3, in both the amount of new customer bookings as well as the revenue contribution from new customers. As usual, churn has remained low, with gross revenue retention stable in the mid to high 90s, highlighting the mission-critical nature of our platform for our customers. Regarding our Q3 financial performance and key metrics, revenue was $886 million, an increase of 28% year-over-year, and above the high end of our guidance range. We ended Q3 with about 32,000 customers, up from about 29,200 a year ago. We also ended with about 4,060 customers with an ARR of $100,000 or more, up from about 3,490 a year ago. These customers generated about 89% of our ARR. We generated free cash flow of $214 million, with a free cash flow margin of 24%. Turning to platform adoption, our platform strategy continues to resonate in the market. At the end of Q3, 84% of customers were using two or more products, up from 83% a year ago. 54% of customers were using four or more products, up from 49% a year ago. 31% of our customers were using six or more products, up from 26% a year ago. 16% of our customers were using eight or more products, up from 12% a year ago. Digital experience is an example of an area with no platform where our rapid pace of innovation is turning into tangible value for our customers. Our digital experience products include RUM, or real user monitoring, to observe and improve application behavior in mobile and web apps, synthetics, to simulate user flows and proactively detect user-facing issues, and product analytics, to help users connect application behavior to business impact. Over the years, we've built up product breadth and depth in this area, and that is being recognized in the marketplace. For the second year in a row, Datadog has been named a leader in the 2025 Gartner Magic Quadrant for Digital Experience Monitoring. We're pleased that today, these digital experience products together exceed $300 million in ARR. This includes, in particular, a very fast ramp for product analytics, which has already seen adoption by more than 1,000 customers. We also want to call out our Security Suite of products, where we are executing and accelerating growth. Security ARR growth was in the mid-50% as a percentage year-over-year in Q3, up from the mid-40% we mentioned last quarter. We're starting to see success in including Cloud SIEM in larger deals, and we'll get back to that in a bit in our customer examples. We're seeing positive trends beyond Cloud SIEM, including fast uptake of code security and an increasing number of wins in cloud security. Overall, we saw year-over-year growth acceleration in each one of our security products. Moving on to R&D, we continue to deliver on what is a very ambitious AI roadmap. We are seeing high customer interest in our Bits AI agents, which we announced at our DASH user conference in June. We have now onboarded thousands of customers for preview access to the Bits AI SRE agent. As we prepare for general availability, we are getting very enthusiastic feedback on the time and cost savings enabled by Bits AI. As one user recently told us, with Bits AI SRE being on-call 24/7 for us, meantime resolution for our services has improved significantly. For most cases, the investigation is already taken care of well before our engineers sit down and open their laptops to assess the issue. This is not an isolated comment. We see the potential here for our agents to radically transform observability and operations. In LLM Observability, we recently launched LLM experiments and playgrounds for general availability, helping teams to rapidly iterate on LLM applications and AI agents. We also launched custom LLM as a judge evaluations for general availability, which lets customers write evaluation prompts to assess application quality and safety. As an illustration of growth and adoption in the past three months, the number of LLM spans customers are sending to Datadog has more than quadrupled. We are seeing a lot of interest in the Datadog MCP server. Our MCP server acts as a bridge between Datadog and AI agents, such as Codex, PowerPoint AI, Cloud Code, Anthropic, Cursor, GitHub Copilot, Groove, BuyBlock, and many more. Our preview customers are using real-time production data context to drive troubleshooting, root cause analysis, and automation in these agents. One user told us, "The Datadog MCP server is a great tool. It enables me to get the last five errors of my app and follow the spans and traces all the way to the root cause. I've never been more hooked on Datadog." We see MCP adoption as a great way to cement Datadog even further into our customers' workflows. Finally, we continue to see rising customer interest for next-gen AI observability, with over 5,000 customers sending us AI data through one or more of our AI integrations. On the topic of integrations, we are very proud to now support over 1,000 integrations, which we believe is unparalleled in our space. By using our integrations, customers correlate otherwise disparate data sources across Datadog products for deeper analysis. We can see from our customers' usage that this is a critical part of the Datadog platform. Our 32,000 customers use more than 50 integrations on average, while customers spending over $1 million annually with us use more than 150. Most importantly, as tech stacks evolve, we continue to update and expand our integrations so our customers can use Datadog to deploy new technologies with confidence. Last but not least, I wanted to give a shout-out to our AI research team for the amazing work they have published. Our Toto open-weights time series for testing model has been one of the top downloads on Hugging Face over the past few months, and that is across all categories. It is very impactful as, among other things, the high quality of this work allows us to attract world-class AI researchers and engineers. Now let's move on to sales and marketing. We had a number of great new logo wins and customer extensions this quarter, so I'll go through a few of them. First, we landed a seven-figure annualized deal with a leading European telco, our largest-ever land deal in Europe. This company's previous setup was expensive, inefficient, and wasn't scOling to meet their needs. By using Datadog, they expect to save over $1 million annually on tool costs alone, along with millions of dollars more in reduced operation costs, lower engineering time, and avoidance of revenue loss. They will adopt 11 Datadog products to start, and will consolidate more than 10 commercial and open-source tools. Next, we landed a seven-figure annualized deal with a leading financial risk and analytics company. The company's fragmented tooling has led to major incidents that sometimes took multiple days and hundreds of engineers to resolve. They plan to start with 11 Datadog products, including On-Call, Cloud SIEM, and Bits AI, and will replace 14 commercial, open-source, and hyperscaler observability tools. Next, we landed a seven-figure annualized deal with a Fortune 500 technology hardware company. This is an exciting win for our new—sorry. This is an exciting win for our new go-to-market motions targeting the largest and most sophisticated companies in the world. Datadog has been chosen as their strategic observability partner, and we are displacing commercial tools across observability, Cloud SIEM, and incident response. This customer is starting with 14 Datadog products. Next, we signed a seven-figure annualized expansion with a Fortune 500 financial services company. This customer had pockets of siloed teams and data, including one business unit which manually hosted and maintained 93 separate instances of open-source tooling. With this expansion, this company will adopt 15 Datadog products, including all three pillars in all of their business units. They will also replace their SIEM solution with Datadog Cloud SIEM in a seven-figure land deal for Cloud SIEM. By bringing all their telemetry data into the Datadog platform, they expect better insights for their adoption of Bits AI SRE agent today and Bits AI security analysis in the next few. Next, we signed a seven-figure annualized expansion with a Fortune 500 heavy equipment company. With this expansion, this customer will replace its open-source log solution with Datadog Log Management and Flex Logs. They plan to adopt LLM Observability, and their IT team is using Cloud Cost Management to improve cost visibility and governance. Next, we will come back, a leading vertical SaaS company, with a seven-figure annualized deal. By returning to Datadog, this customer benefits from more alignment with OpenTelemetry, and will implement the incident and reliability processes that they were unable to execute on previously. Next, we signed a seven-figure annualized expansion with a major American carmaker. This customer is adopting Datadog products faster than previously expected, and this agreement supports their higher usage. With this expansion, they will adopt Datadog's Incident Management and On-Call solution company-wide for a total of 5,000 users who support operational continuity across the business. Finally, we signed a nine-figure annualized expansion with a leading AI company. This company has been a longtime Datadog customer and has expanded their usage of multiple products, securing better economics for a higher commitment with an early renewal. Speaking of AI customers, we continue to help AI-native customers, big and small, to grow and scale their businesses. We continue to see this group broaden in number and size, with more than 500 AI-native companies in this group, about 100 of which are spending more than $100,000 annually with Datadog, and more than 15 who are spending more than $1 million annually with us. While we know there's a lot of attention on this quarter, we primarily see it as an indication of what's to come as companies of every size and every single industry incorporate AI into their cloud applications. That is it for another very strong quarter for more go-to-market teams who are now very hard at work, as we have a really exciting pipeline for Q4. Before I turn it over to David for a financial review, I want to say a few words on our longer-term outlook. There is no change to our overall view that digital transformation and cloud migration are long-term secular growth drivers of our business. Meanwhile, we are advancing rapidly in AI, where we are incredibly excited about our opportunities. We are building a comprehensive set of AI observability products to help our customers tackle the higher complexity that comes with these technologies. We're building AI into Datadog, and I spoke earlier about the excitement our customers have for Bits AI agents. The market opportunity in cloud and AI is expected to grow rapidly into the trillions of dollars, and companies of every size and industry are looking to adopt AI to deliver value to their customers and drive positive business outcomes. We're moving fast to help our customers develop, deploy, and grow into the cloud and into the AI world. With that, I will turn it over to our CFO, David. Thanks, Olivier. To start, our Q3 revenue was $886 million, up 28% year-over-year and up 7% quarter-over-quarter. To dive into some of the drivers of our Q3 revenue growth, first. Overall, we saw sequential usage growth from existing customers in Q3 that was higher than our expectations and the strongest in 12 quarters in our non-AI native customer base. We saw year-over-year growth acceleration broadly across our business, including in new logos and existing customers, both enterprise and SMB, with customers across our spending bands, big and small, and customers in a wide variety of industries. Next, we saw strong and accelerating contribution from new customers. New logo annualized bookings more than doubled year-over-year and set a new record driven by an increase in average new logo land size, particularly in enterprise. We believe we are starting to see the benefits of our growth of sales capacity. We are seeing new logos ramping faster and contributing more to revenue growth. The portion of our year-over-year revenue growth that related to new customers was about 25% in Q3, up from 20% in Q2. Next, our AI-native customers continue to exhibit rapid growth, while more customers in this group are growing to be sizable customers. As Olivier discussed, we extended the contract of our largest AI-native customer. In addition, we now have more larger AI customers, including 15 of them spending $1 million or more annually with Datadog and about 100 spending more than $100,000 annually. Year-over-year revenue growth from our AI-native customers, excluding the largest customer, again accelerated in Q3. In Q3, this group represented 12% of our revenue, up from 11% last quarter and about 6% in the year-ago quarter. I will note that over time, we think this metric will become less relevant as AI usage in production broadens beyond this group of customers. Our year-over-year revenue growth also accelerated amongst our non-AI-native customers. In Q3, our revenue growth, excluding the AI-native customer group, was 20% year-over-year, accelerating from 18% year-over-year in Q2. We have seen this trend of accelerating growth continue in October. Regarding retention metrics, our trailing 12-month net revenue retention percentage was 120%. Similar to last quarter, our trailing 12-month gross revenue retention percentage remained in the mid to high 90s. Now moving on to our financial results. Our billings were $893 million, up 30% year-over-year. Our remaining performance obligations, or RPO, was $2.79 billion, up 53% year-over-year, and current RPO growth was in the low 50% year-over-year. Our strong bookings contributed to this acceleration of RPO. We continue to believe that revenue is a better indication of our trends in our business than billings and RPO. Now let's review some of the key income statement results. Otherwise, note all metrics are non-GAAP. We have provided a reconciliation of GAAP to non-GAAP financials in our earnings release. First, gross profit in the quarter was $719 million, and our gross margin was 81.2%. This compares to a gross margin of 80.9% last quarter and 81.1% in the year-ago quarter. As previously mentioned, we continue to see the impact of our engineers' cost-saving efforts increase Q3 as they deliver on our cloud efficiency projects. Our Q3 OpEx grew 32% year-over-year, down from 36% last quarter. We continue to grow our investments to pursue our long-term growth opportunities, and this OpEx growth is an indication of our execution on our hiring plan. Q3 operating income was $207 million, for a 23% operating margin compared to 20% last quarter and 25% in the year-ago quarter. Now turning to our balance sheet and cash flow statements, we ended the quarter with $4.1 billion in cash, cash equivalents, and marketable securities. Cash flow from operations was $251 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $214 million. For a free cash flow margin of 24%. Now for our outlook for the fourth quarter and the fiscal year 2025. 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. For the fourth quarter, we expect revenue to be in the range of $912 million-$916 million, which represents a 24% year-over-year growth. Non-GAAP operating income is expected to be in the range of $216 million-$220 million, which implies an operating margin of 24%. Non-GAAP net income per share is expected to be in the range of $0.54-$0.56 per share, based on approximately 367 million weighted average diluted shares outstanding. For the full year, fiscal year 2025, we expect. Revenues to be in the range of $3.386 billion-$3.390 billion. This represents 26% year-over-year growth. Non-GAAP operating income is expected to be in the range of $754 million-$758 million, which implies an operating margin of 22%. Non-GAAP net income per share is expected to be in the range of $2-$2.02 per share, based on 364 million weighted average diluted shares. Finally, some additional notes on our guidance. We expect net interest and other income for the fiscal year 2025 to be approximately $170 million. We continue to expect cash taxes in 2025 to be about $10 million-$20 million, and we continue to apply a 21% non-GAAP tax rate for 2025 and going forward. Finally, we expect capital expenditures and capitalized software together to be 4% of revenues in fiscal year 2025. To summarize, we are pleased with our execution in Q3. We are well-positioned to help our existing and prospective customers with their cloud migration and digital transformation journeys, including their adoption of AI. I want to thank Datadog's worldwide for their efforts. With that, we'll open the call for questions. Operator, let's begin the Q&A. Thank you. At this time, we'll conduct a question-and-answer session. As a reminder, to ask a question, you'll need to press star one one on your telephone and wait for your name to be announced. To withdraw your question, please press star one one again. Please stand by while we compile the Q&A roster. Our first question comes from the line of Kash Rangan of Goldman Sachs. Your line is now open. Hi. Thank you very much. Appreciate it. Congratulations on the spectacular results and the showing of sequential improvement across the board. Olivier, I had a question for you. We've talked about GPU monetization versus CPU monetization. How closer are we to the point where you can confidently expand and get your share of the customer wallet when it comes to whether it's training workload, inferencing workload on the GPU clusters, which are becoming more prevalent and increasingly a larger part of the compute build-out in the future? That's it for me. Thank you so much. Yeah. We have products that are getting into the market now for GPU monitoring, but these don't generate any significant revenue yet. All the revenues we've shared, like the acceleration, etc., that's not related to us capitalizing more on GPUs. It's a future opportunity. Thank you. One moment for our next question. Our next question comes from the line of Sanjin Singh of Morgan Stanley. Your line is now open. Yeah. Thank you for taking the questions, and congrats on the acceleration in growth this quarter. Olivier, I wanted to talk about some of those enterprise trends you're seeing in sort of your non-AI cohort. What do you sort of put the improved performance and growth this quarter on? You mentioned that the sales productivity or the benefits from some of the sales investments are starting to come online. Is there sort of an uplift in sort of the cloud migration trends? Are you starting to see enterprise build more AI applications? I just love to get your perspective on the underlying trends in the enterprise and the mid-market business. Yeah. I'd say there's three parts to it. One part is the demand environment is positive in general. I don't know that we see massive acceleration of cloud migration, but at least the environment is not pushing the other way. We know which happens from time to time. That's point number one. Point number two is, we've been growing sales capacity quite a bit, and we've created new go-to-market motions to grow up to the kind of customers we were not getting before. We've done quite a bit of investment over the past couple of years, and we see that starting to pay off. As I said, also, we feel good about the Q4 in terms of pipeline on the sales side. It's too early to tell yet. We still have to close those deals, but we feel good about the scaling of our go-to-market. Point number three is, we have a number of products that we've been developing over the years, some of them early, some of them a little bit further along, that are really clicking. We see. We have a lot of success with getting larger prices to adopt Flex Logs, for example. We have a lot of success with some of new products such as Product Analytics that we mentioned on the call. We're seeing some large land deals with our cloud team. All of that is contributing to the picture you're seeing today. Just as a follow-up on the AI observability opportunity, when you look at some of the independent software vendors that are releasing agentic solutions, agentic portfolios, a number of them are including observability as part of their sort of value proposition. Is there any work you think Datadog has to do to sort of infiltrate that market or make sure that customers look to Datadog as that agentic monitoring capability as some of these independent software vendors try to bundle in observability into their solutions? I would love to hear your perspective on that. Yeah. I mean, there's absolutely no doubt to us that the customers will need and want a unified platform for observability for this. There's two parts to that. One is, historically, every single piece of software we integrate with, whether that's SaaS or things that customers run themselves, also has its own management console and observability console. You're not going to log into 17, or in the case of our customers we mentioned, they use 60 integrations for the smaller customers, 150 integrations for the larger ones. It's not practical to actually go and manage that separately. We think all of that belongs in a central place. That's the historical trend we've seen. We also think that you can't separate the AI parts from the non-AI parts of the business. You're not going to look at your agents separately like you do at your web hosting and your database and everything else that you have in your stack. All of that, in the end, will be attached to observability. Very clear. Thank you very much. Thank you. One moment for our next question. Our next question comes from the line of Raimo Lenschow of Barclays. Your line is now open. Perfect. Congrats from me as well. That sounded like an amazing quarter and nice to see it coming together. On the AI side, and I don't want to talk about the big customer, but more the other ones, like 15 customers over $1 million, that's like a big number, and over $100,000. How do we have to think about the nature of those? Are those kind of, especially the bigger ones, those kind of model builders? Then even 15 is a big number, and over 100 sounds like this whole new application world that we've all been kind of waiting for starting to come together. Is that kind of what's going on there? Because it does sound quite exciting and much more broad than we thought. Thank you. It's actually fairly broad. There are model vendors. There are models. Models that can be LLMs, models that can be video, it can be sound generation, it can be all of the various parts of the stack you see as independent companies. It can be quite a few companies that work on the coding side, so coding assistants and vibe coders and everything in that range. Some of these are very new companies. Some of these are not very new companies. Some of these started five, seven, eight years ago, and were sort of not necessarily AI native from day one, but very quickly would give them the growth they see today with the pivot to AI. We see the reason of that. We have companies that are other parts of the stack in AI, on the, say, the serving side, the other components of the infrastructure. We have other companies that are purely applications filled with AI. We have a bit of everything in there. It's actually fairly representative of the space. Okay. Perfect. That's exciting. Thank you. Thank you. One moment for our next question. Our next question comes from the line of Mark Murphy of JPMorgan. Your line is now open. Oh, thank you so much. You had mentioned the expansion of the contract with your largest AI native customer, and I believe you said better economics for a higher commitment. Can you speak to that? Because I would assume a higher commitment would carry a volume-based discount. I'm just trying to understand. For some reason, if that was not the case here, what did you mean by better economics? And then I have a quick follow-up. Yeah. Yeah. I mean, look, without getting into the detail of any specific customer like this, the motion is always the same. Customers grow. They commit to more. They get better prices. You see, again, talking about customers in general, you see growth of usage, drops in revenue as customers renew and get a higher commit and a better price, and then usually growth after that for those customers. That's the. Motion that we've had with about 30,000 customers so far. Okay. The better economics part of it is where it's going to be netting out like 12 months down the road. Is that what you mean? The bigger economics means you commit to more, you get a better price. As we remember, we have a usage model, so we charge people every month on what they use at the price we agreed. If you get better economics and your usage is somewhat similar month to month, from month to next, you pay less. The overall backdrop of our business is increased consumption. Okay. As a quick follow-up, Olivier, the acceleration that you saw in security growth is pretty noticeable too. We recall, I think, about six months ago, you had ramped up and engaged a lot more with channel partners, which is a key ingredient to growing a security business. Is it a function of that, or is there a mindset change happening out there where customers want observability to be the central point of collection so that all the security teams and the ops teams are working with the same set of metrics and logs and traces? Look, I think it's a number of things. Definitely, we've been investing in the channel. That's certainly helpful to the security business as a whole. The big wins we mentioned on security, that we mentioned a couple of wins in the Cloud SIEM, these tend to be more related to product maturity. The strength of our underlying platform, especially when it comes to technology like Flex Logs, for example. The fact also that we've been learning how to properly go-to-market for security. I think we still been clicking in a way that is exciting. Thank you. Thank you. One moment for our next question. Our next question comes from the line of Fatima Boolani of Citi. I'm assuming your line is now open. Good morning. Thank you for taking my questions. Oli, I'll start with you and have a follow-up for Dave. On the on-call product, Oli, how do agentic advancements in general detract or enhance the value proposition here? I'm very simplistically thinking about the core nature and value proposition of the on-call product, intelligently routing requests for remediation. Right? How do just broader advancements in AI help beef up and/or detract your ability to monetize this product? Then just a follow-up for David, please. I mean, look, if you zoom out, we entered the field with On-Call because we wanted to own the end-to-end incident resolution. We wanted—before that, we were detecting the incidents and sending the alerts. Then we were pretty much where the resolution happened after that. Customers were spending that time in data to diagnose and understand what was going on. We wanted to own the full cycle. We thought that with AI, in particular, we'd have the ability to do things if we owned the whole cycle that we couldn't do otherwise. What you see right now is, I mean, this resonates with customers. They're adopting the product. We've mentioned some exciting customers with one with 5,000 seats for On-Call, which is very exciting. In the future, there's many more things we can do and we're working on for that product. If we. Both detect the incident and notify. We can do some subtle things such as even predicting the incident and notifying early, or rerouting early, or telling people before the incident actually takes place how they can potentially fix it. These are all things we're working on. I mean, look, if you look at the various product announcements we've made, whether that's Bits AI, SRE, or the time-series forecasting model we've released, when you assemble all that, you get to a very, very interesting picture of what we can do in the future. We're excited by that. Our customers are excited by the vision there too, and that's why this product is successful. Appreciate that. David, on net retention rates, why aren't we necessarily seeing more upward pressure on the metric, just given the strength of expansionary bookings that you alluded to in the quarter from the install base? I mean, I suspect it's because it's a trailing 12-month metric, but any directional color you can just share on that and any high-level commentary on some of the non-AI native net retention rate trend behavior. Thank you. Yeah, you've nailed it. It's a trailing 12 months. It's a number that's rounded. It has the dynamics that you might expect in that. The growth of the non-AI natives has been, as we mentioned, a combination of landing and expanding at higher rates than we've seen in recent quarters. If that continues, as you go into a trailing 12-month metric, you see a directional movement. Thank you. Thank you. One moment for our next question. Our next question comes from the line of Eric Keith of KeyBank. Your line is now open. Hey, great. Thanks for taking the question. Oli, David. AI seemed like a really exciting thing out of Dash. I know it's still in preview, but you mentioned there's a lot of interest there. I'm just curious how you think about the agentic opportunity with Bits AI and how meaningful this can be for 2026 as a differentiator versus competition and also as a revenue contributor. Thanks. Yeah. I mean, look, it's super exciting. The feedback's very good on it. We've been collecting all the—so I read one quote. We have dozens that look just like that that were sent to us by customers. That's very, very exciting. We also started having some customers buy and come to it just to show value and to make sure we were onto the right product mix. We feel good that this is something that is high-quality and we can monetize. In terms of the impact for next year, on the packaging side, I'm not completely sure yet whether the biggest impact will be seen from what we charge for Bits AI itself or for the rest of the platform that it gets benefits from the differentiation of Bits AI. I think that's more of a broader question of packaging and monetization of AI. Remember that we have a product that is usage-based. Anything that drives usage up and adoption from customers is good for us and is very, very monetizable. What we can tell is this is differentiating. This is good. It works significantly better than anything else we've heard of in the market. We're doubling down on it. We have many, many teams now working on deepening Bits AI SRE to make sure it goes further into the resolution. Doesn't just point to the issue but fixes the code, all these kind of things. We're working hard on that. We're also working on breadth, making sure that we train it on many more types of data, many types of sources, sometimes even systems that are observed, systems that are not Datadog. We can cut across to other systems our customers are using. We're very, very aggressively developing Bits AI SRE. It's resonating very well in the market. Thank you. One moment for our next question. Our next question comes from the line of Gray Powell of BTIG. Your line is now open. Oh, great. Thanks for taking the question. And congratulations on the great results. Maybe just taking a step back. If we go back to the beginning of the year, Datadog was expecting 19% revenue growth. It looks like you're tracking to something over 26% growth now, and that's just the high end of your guidance. I guess my question is, what surprised you the most this year? How do you feel about the sustainability of those drivers as you look forward? I mean, look. First, I apologize for overdelivering on the results. We might do it again, but we'll see. I think the biggest surprise for us has been that AI in general has, or AI adoption has, grown faster than we thought it would at the beginning of the year. We've seen that across our AI cohort. We've also seen that we got some of our new products and the changes we were making on the go-to-market side to click perhaps earlier than we would have thought otherwise. All in all, we saw the leading part of the business with AI grow faster. The, not the lagging, but the slower growing, more traditional part of the business also accelerate. That gets us where we are today. I'd add, we have a good demand environment. We've been investing, whether it be in the products that Oli's been talking about or in the sales capacity. We made clear that we were an investor and we're seeing those investments pay off. All right. That's helpful. Thank you. Thank you. One moment for our next question. Our next question comes from the line of Koji Ikeda of Bank of America Securities. Your line is now open. Yeah. Hey, guys. Thanks so much for taking the question. Just one from me here. I wanted to ask a question on the inflection in the non-AI native growth and how to think about the areas of strength in this cohort. Is it coming from your largest enterprises? Is it coming from a certain type of customer? Is there a common theme in the workloads that you're seeing or the products that are being added on that is driving that strength, or is it just really just broad-based? What I'm trying to get at here is I'm really trying to understand more the durability of this growth inflection. Thank you. It is broad-based. I think, again, it speaks to a couple of things. It speaks to the fact that, in general, the demand environment is good. Though I would say there's been a very, very high growth of hyperscaler revenue over the past, or an acceleration for the hyperscalers in general. A lot of that is GPU-related, but the growth we're seeing here and the acceleration we're seeing here is largely not GPU-related. There's a little bit of it, but not a ton of it. That's not exactly what you've seen with some of the other vendors there. One reason this is broad-based is these are the same products we sell to all customers. This is largely the same go-to-market organization, though we have a few segments, and we've been doing well at executing there. I think we've invested quite a bit in product, and we will keep doing it, and we see the results of that. Yeah. I'll add that it's across the customer base, enterprise, SMB. When we look at it, it's not just an AI SMB. If you remove those AI companies, you still see a strengthening SMB demand cycle going on. Unlike in previous periods, it also is across spending ranges. We're not seeing larger spenders or smaller spenders. We're just seeing a broad trend of improved demand across the spending trends. Remember that for us, SMB is any company of less than 1,000 employees. It includes a lot of very legitimate and growing businesses. It's not just anti-stop. Thank you. Thank you. One moment for our next question. Our next question comes from the line of Ittai Kidron of Oppenheimer & Co. Your line is now open. Thanks. And congrats, guys. Really great numbers. Oli, in your answer to one of the questions and kind of going into the drivers behind the upside, you talked about sales capacity increase. You didn't talk much about sales efficiency. Is there a way you can give us some color on where do you stand on percent of salespeople that are hitting quota? Where does that ratio stand relative to historical patterns for you guys? As you approach 2026 here, do you anticipate any material changes in the comp structure, just given the breadth of product and their list of opportunities? How do you get people focused? Yeah. Look, we feel good about the sales productivity in general. The rule, generally, is you grow by scaling capacity and maintaining productivity. It's hard to drive both up at the same time. Remember, if you want to grow to 10x, you can do that by scaling. You can't really do it by improving productivity, so you have to scale. We've been doing that, and we've been successful at it so far. In terms of the compliance, look, we keep changing the way. We compensate and the way we manage the salesforce in general to make sure we have the right focus. One of the gifts of a business model like ours is that we have a very heavy blend mix plan model. We get a lot of growth from meeting customers. The challenge in Create, on the other hand, is how do we get to focus the salesforce on the newer customers, the smaller ones, and the new ones? It is more work to get an extra dollar for a smaller customer or for another new one than it is from an existing one that they already have scaled. A lot of the tweaks we make to our compliance relate to that. Who do we grow? Who do we grow? Who do we make sure we direct our attention? We reward people for what is going to generate the most long-term growth for us. We have made a number of changes. I will not go through them. These are internal changes. We made a number of changes this year. We see a number of them pay off. Another thing I mentioned on the call was we mentioned the wins for one of our new go-to-market motions. That is specifically getting in place multi-year plans to go after some larger customers that are tougher to land than what we have done in the past. Sometimes it takes more than a year to land certain types of customers. The problem is if your comp plan is on the heads of one-year horizon, it does not give a great incentive for the salesforce to go after those customers. We cordoned off a few of those companies who have special plans to go after that. We started to see success with that too. This is just an example. Appreciate it. Thank you. One moment for our next question. Our next question comes from the line of Andrew Sherman of TD Cowen. Your Your line is now open. Oh, great. Thank you. Congrats. I know you have a team focused on the Fortune 500, where there is still a lot of white space for you. Curious to hear how that team's ramping to productivity. Did that help drive some of the strong new logo bookings? Can this contribute even more next year? Thanks. Yeah. I mean, look, the key is not new, right? We have been focusing on that for many years. We are tracking well. One thing I was mentioning just before was one challenge even in the Fortune 500 is to make sure that we focus on landing new customers and make sure that there's the right amount of sales attention and reward for the landing a customer, even if it's for a small amount. I think we've done well. I mean, again, we can comment on that again after the next quarter when we have a full year of our new plans that have been validated. So far, we feel very good about it. Thank you. One moment for our next question. Our next question comes from the line of Alex Zukin of Wolfe Research. Your line is now open. Yeah. Hey, guys. Thanks for taking my question. And congrats on dropping some truly inspiring quotes in the script. Maybe, Oli, one for you, and then I have a quick follow-up for David. Just the duration of this. Acceleration of the non-AI cohort, it seems like from all your forward-looking metrics, whether it's billings, RPO, CRPO, those were, again, really, really strong. How long do you think we should think about the duration of this. Trend of this non-AI acceleration? You know we're a consumption business. The hardest thing to understand is what the future is going to look like for consumption. The way I would say it is we feel very good about it at the midterm, long term. Now, how it ebbs and flows in a given month or quarter, that's harder to tell. Again, that's what we've seen through the life of the company. What we feel very confident about, though, is the motion in general for. Digital transformation and cloud migration is steady. Sometimes it slows down a little bit, but it reaccelerates after that. We see that going on for a very long time. Maybe just, David, for you, look, gross profit dollar acceleration while you're seeing your largest customer kind of get better unit economics is also inspiring to see. How should we think about the progression of gross margins and gross profit dollar growth, particularly as you continue to also see the AI cohort acceleration? Yeah. There's a couple of things. I think we've mentioned that we've been focused and have focused over the many years on the efficiency of our cloud platform. We have significant engineering efforts around cost of sales and delivery of value. We've been able to deliver on that. We also have a very broad customer base distributed in terms of volume. As customers get larger and maybe get volume discounts, we have a number, a lot of customers coming in at smaller, so that balance there. In terms of the future, I repeat what we've always said, that we've been running the company with a gross margin ± 80. We've given that range and not changed it. We watch it, and it gives us signals in terms of efficiency, how we're operating. It gives us signals in pricing and things like that. I wouldn't change the comments we made over the many years about looking at that and then developing operations and strategies around that. Perfect. Thank you, guys. Yep. Thank you. One moment for our next question. Our next question comes from the line of Ryan MacWilliams of Wells Fargo. Your line is now open. Hey, thanks for taking the question. Just one for me. On the large AI contract expansion that you provided commentary on, is there any way we can think about the contribution change from this customer over the next few quarters? Thanks. No. I mean, we do not provide that kind of information on individual customers. We are trying to give a picture of the overall business. Generally, I think, as Oli mentioned, on our larger customers, we have a motion of the expansion of volume. And then we work on the term and the volume-based pricing. But we do not give guidance like that on individual customers. Fair enough. Thanks. Thank you. One moment for our next question. Our next question comes from the line of Mike Cikos of Needham. Your line is now open. Great. Thanks for taking the questions, guys. I just wanted to come back to it, Oli. For the non-AI native strength, I know we've kind of hit on this a number of times, whether it's roadmap, sales capacity, execution, but kudos on the numbers here. I'm just trying to get a better sense of the why now. Is it just a composite of all those different pieces clicking together this quarter, or is there anything more to unpack there? I have a follow-up for David. Again, I don't think there's a lot more to unpack there. I know it's boring in a way, but it's also the way we've been growing for the past 15 years really. That's the, I would call it, the usual. Awesome. Awesome to hear. Okay. For the follow-up to David, David, I don't want to take anything away from the Q3 results you guys just posted. We obviously have the strong guide here for Q4. I just can imagine myself a month from now starting to get inbounds from certain folks asking about the holiday season and the fact that the holiday is landing on weekdays in Q4 here. Can you just kind of discuss how you thought about constructing guidance for this Q4 year? Yeah. We have years of experience of analyzing the day-by-day patterns. In the holidays, we know that the holiday period ends up in the usage side because of vacation holidays. And we incorporate that into our guidance. I think evolved a lot over the years and sort of days adjusted, types of days, etc. We would be incorporating that like we've incorporated in other years. If there are differences in this calendar period, we incorporate that as always. Very helpful. Thank you, guys. Thank you. One moment for our next question. Our next question comes from the line of Karl Kierstad of UBS. Your line is now open. Okay. Great. Thank you. I'll ask one for David and one for Olivier. David, first of all, congratulations on the extension of the larger contract. I think everybody on the line is applauding that. I know you're reticent to get into any details, but maybe I could try. Are you able to clarify whether that was a one-year deal or multi-year? And then related to that, David, what is the contribution to CRPO from that deal, which I presume landed in your CRPO number? If it is a one-year deal, does the entirety of that contract contribute to the sequential CRPO performance in the quarter? That's it for you, David. And then, Olivier, maybe I'll just ask both at once. Some of the very large AI natives are beginning to diversify to utilizing Oracle's OCI and Stargate. I'm wondering, what's the opportunity for Datadog to essentially follow that behavior and begin scaling on Oracle, Stargate, or because a lot of what Oracle is doing with the AI natives is training clusters, perhaps that near-term opportunity is more limited. Thank you both. Yeah. On the first point, I think we give a lot of examples. Our motion, which our customers would be following, including that one, would be we fix out annual-plus commits. We're not commenting on individual contracts here. It would follow a typical path to other types of contracts. That's what we would do. Yeah. On OCI, look, we've built an OCI integration, and we see more demand from customers on OCI. Some of the things we see, like the Stargate, etc., these are extremely custom-built apps. I don't know. They're not necessarily exactly cloud because they're custom-built for specific customers. The opportunity there is more remote today. One company is that it's a not fantastic opportunity to productize. If 10, 15, 20, 50 companies start using that, then that really becomes a commercial opportunity. We are very much plugged into all of that. We go basically where our customers are. I think you mentioned about the RPO. No, I think in this case, we've mentioned that this current and the total is roughly the same. There wouldn't be anything in that contract that would have been materially around those numbers. Those numbers, I think we mentioned, are produced from the bookings growth more generally and not from that particular contract. Thank you. Thank you. One moment for our next question. Our next question comes from the line of Jake Roberge of William Blair. Your line is now open. Yeah. Thanks for taking the question. Just on the recent go-to-market investments, obviously, it seems like there's been a lot of traction thus far with those. I'm curious if there are any areas like security or the new logos or upmarket that you could look to lean even deeper into, just given the growth that you've seen here. Yeah, definitely. That's something we didn't do this year that we were definitely going to do next year. There's a number of things. We're in Q4, right? We're in the middle of planning for next year. Basically, we'll keep scaling what's working, stop doing some of the things that are not conclusive, and then try a few more things. That's the way it works. Interestingly enough, building your go-to-market is not that different from building software. You experiment, you gather data, you see what's working, what's not working, and you build the systems. That's helpful. Just on the new bits of AI agents, can you just talk about the early feedback that you've gotten for those solutions and maybe how the engagement with those agents has compared to kind of the ramp of security Flex Logs? I know, obviously, much earlier days, but just how it compared when those were still largely in the preview phase. I mean, Bits AI sre agent really has a role factor for customers. What works really well is, and we've seen that a number of times, we set it up for them, it's running on their alerts, and they go through an outage, and they still go through the motions. They still set up a bridge, and they have 20 people, and they spend two hours. In the end, they have an idea of what went wrong. Then they go to Datadog, and they see, "Oh, wait, there's an investigation that had run." Three minutes into the outage, it got the same conclusion that we got two hours later with 20 people on the call. That is completely eye-opening for customers when they see that. That is why we get many quotes about it. Now, there is more we need to do there. Customers say, "Oh, this is great. Now, can you make the fix for me? Can you do this? Can you do that? Can you support that other system that right now you can't actually set it up for? We have a very, very full roadmap of things we need to do, and we're doubling down on it. We also shipped—I mean, this one is in preview—but we shipped a security agent that looks at vulnerabilities and looks at security signals and does triage, basically looking at trying to investigate what might be benign or what might be a real issue. We also are getting very, very positive feedback for that. In fact, that's what helped us win some large lenders for our Cloud SIEM products. The combination of the SIEM that runs extremely efficiently on top of observability data, that runs very efficiently on top of Flex Logs, but also saves an immense amount of time by getting 90% of the issues out of the way with automated investigations. That's extremely attractive to customers. All right. I think with that, we're going to close the call. Before we go, I just want to give one quick shout-out to the team because I know, as I said earlier, we have quite a lot going on in Q4, whether it's on the planning side, on the product-building side, or on the sales side, where I said we have a really, really exciting pipeline. We have a lot to do. I want to thank the team for the hard work there. I'm looking forward to meeting a lot of our existing and new customers at AWS re:Invent in a few weeks. I'll see you all there. Thank you all. Thank you for your participation in today's conference. This does conclude the program. You may now disconnect.