Good day, and thank you for standing by. Welcome to the Third Quarter 2025 Datadog Earnings Conference Call. Please be advised that today's conference is being recorded. I would now like to hand the conference over to your first speaker today, Yuka Broderick, Senior Vice President of Investor Relations. Please go ahead.

Thank you, Martin. 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 a discussion of the material risks and other important factors that could affect our actual results, please refer to our Form 10-Q for the quarter ended June 30, 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, and 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 ongoing strength in cloud migration and digital transformation. Against this backdrop, we executed 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. And 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. And 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%. Our platform strategy continues to resonate in the market. At the end of Q3, 84% of customers were using 2 or more products, up from 83% a year ago. 54% of customers were using 4 or more products, up from 49% a year ago. 31% of our customers were using 6 or more products, up from 26% a year ago and 16% of our customers were using 8 or more products, up from 12% a year ago. Digital experience is an example of an area within our 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 stimulate user flows and proactively detect user-facing issues and product analytics to help users connect application behavior to business impact. Over the years, we built our 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 the leader in the 2025 Gartner Magic Quadrant for Digital Experience Monitoring. We are 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-50s as a percentage year-over-year in Q3, up from the mid-40s 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. And we're seeing positive trends beyond Cloud SIEM, including fast uptake of good security and an increasing number of wins in cloud security. Overall, we saw year-over-year growth acceleration in each 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, the Bits AI SRE agent. As we prepare for general availability, we are receiving very enthusiastic feedback on the time and cost savings enabled by Bits AI. As a RUM user recently told us, "with Bits AI SRE being on call 24/7 for us, meantime to 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. And 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 for judgment 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 few months, the number of LLM span customers sending to Datadog has more than quadrupled. We are also seeing a lot of interest in the Datadog MCP servers. Our MCP server acts as a bridge between Datadog and AI agents, such as Codex OpenAI, Claude by Anthropic, Cursor, GitHub Copilot, Goose by Block 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 5 of my app and follow the spans and traces all the way to the root cause. I have never been more hooked on Datadog." So 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 to one or more 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 can correlate otherwise disparate data sources across Datadog products for deeper analysis. We can see from customer 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 OpenWave time-series forecasting 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 in customer expansion this quarter. So I'll go through a few of them. First, we landed a 7-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 scaling to meet their needs. By using Datadog, they expect to save over $1 million annually on tool cost 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 we consolidate more than 10 commercial and open source tools. Next, we landed a 7-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 hyperscale observability tools. Next, we landed a 7-figure annualized deal with a Fortune 500 technology hardware company. 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 availability, cloud team, and incident response. This customer is starting with 14 Datadog products. Next, we signed a 7-figure annualized expansion with a Fortune 500 financial services company. This customer has 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 3 pillars in all of their business units. They will also replace their SIEM solution with Datadog Cloud SIEM in a 7-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 Agents today and Bits AI tools. Next, we signed a 7-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. We will come back to a leading vertical SaaS company with a 7-figure annualized deal. By returning to Datadog, this customer benefits from our alignment with open telemetry, and we'll implement the incident and reliability processes that they were unable to execute on previously. Next, we signed a 7-figure annualized expansion with a major American carmaker. This customer is adopting Datadog products faster than previously expected, and this agreement supports the higher usage. With this expansion, they will adopt Datadog 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 9-figure annualized expansion with a leading AI company. This company has been a long-time 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 cohort, 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's it for another very strong quarter from our 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're building a comprehensive set of AI Observability products to help our customers tackle the higher complexity that comes with these technologies. And we are building AI into Datadog, and I spoke earlier about the excitement our customers have for our 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. So 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 contributions 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, 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 among 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, and 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, and our trailing 12-month gross revenue retention percentage remains in the mid- to high 90s. And 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 50s percentage 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. And now let's review some of the key income statement results. Unless otherwise noted, all metrics are non-GAAP. We have provided a reconciliation of GAAP to non-GAAP financials in our earnings release. 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 in Q3 as they deliver on our cloud efficiency project. 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 of 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. And 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 and 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 velocity overall remains unchanged. 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 to $916 million, representing a 24% year-over-year growth. Non-GAAP operating income is expected to be in the range of $216 million to $220 million, implying an operating margin of 24%. Non-GAAP net income per share is anticipated to be in the range of $0.54 to $0.56 per share based on approximately 367 million weighted average diluted shares outstanding. For the full fiscal year 2025, we expect revenues to be in the range of $3.386 billion to $3.390 billion, which represents 26% year-over-year growth. Non-GAAP operating income is forecasted to be in the range of $754 million to $758 million, implying an operating margin of 22%. Non-GAAP net income per share is expected to be in the range of $2 to $2.02 per share, based on 364 million weighted average diluted shares. Additionally, we expect net interest and other income for fiscal year 2025 to be approximately $170 million. We anticipate cash taxes in 2025 to be about $10 million to $20 million and we will continue to apply a 21% non-GAAP tax rate for 2025 and beyond. We also expect capital expenditures and capitalized software together to be 4% of revenues in fiscal year 2025. In summary, we are pleased with our execution in Q3. We are well positioned to assist our existing and prospective customers with cloud migration and digital transformation journeys, including AI adoption. I want to thank Datadog employees worldwide for their efforts. Now, we'll open the call for questions. Operator, let's begin the Q&A.

Our first question comes from Kash Rangan of Goldman Sachs.

Appreciate it. Congratulations on the spectacular results and showing sequential improvement across the board. Olivier, I had a question for you. We've talked about GPU monetization versus CPU monetization. So 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.

Yes. So we have products that are getting into the market now for GPU monitoring. But these don't generate any significant revenue yet. So all the revenues we've shared, like the acceleration, etc., that's not related to us capitalizing more on GPUs; that's a future opportunity.

Our next question comes from the line of Sanjit Singh of Morgan Stanley.

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 in growth this quarter on? You mentioned that the sales productivity or the benefit from some of the sales investments starting to come online. Is there sort of an uplift in sort of the cloud migration trends as you're starting to see enterprises build more AI applications? I'd just love to get your perspective on the underlying trends in the enterprise and mid-market business.

I would say there are three key aspects to consider. First, the overall demand environment is generally positive. While we may not be witnessing a significant acceleration in cloud migration, the conditions are not unfavorable as they can be at times. Second, we have made substantial investments in increasing our sales capacity and have developed new strategies to target customers we previously overlooked. We are starting to see the returns on these investments, and we are optimistic about our sales pipeline for Q4. Although it's premature to draw definitive conclusions as we still need to finalize some deals, we are pleased with the growth of our go-to-market efforts. Third, we have been developing a range of products over the years, with some at different stages of progress. We're experiencing notable success in getting large enterprises to adopt products like Flex Logs, as well as new offerings such as analytics. Our cloud team is also securing significant deals. All these factors are contributing to our current success.

And 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, 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 get your perspective on that?

Yes. We have no doubt that customers seek a unified platform for observability. There are two aspects to consider. Firstly, every piece of software we integrate with, whether it is SaaS or on-premises, tends to have its own management and observability controls. However, it's impractical for users to log into 70 different systems—or, in the case of some customers, 60 for smaller ones and 150 for larger ones—just to manage them separately. Therefore, we believe all of this should be centralized, and that has been the historical trend we've observed. Additionally, we feel that AI capabilities cannot be separated from non-AI components of the business. You won't manage your agents distinctly from your web hosting, databases, and other elements in your tech stack. Ultimately, all of this will be integrated with observability.

Our next question comes from the line of Raimo Lenschow of Barclays.

Congratulations to everyone for what sounds like a remarkable quarter; it's great to see everything coming together. On the AI front, without focusing on any particular customer, the fact that there are 15 customers contributing over a million is significant, and over 100 with contributions exceeding 100,000 is impressive. How should we interpret the nature of these customers? Are many of the larger ones involved in model building? Even having 15 is noteworthy, and more than 100 suggests a new landscape of applications that we've been anticipating is starting to materialize. Is that the case? It seems quite exciting and broader than we initially thought.

It's actually quite broad. There are various model vendors and models that can include lens models, video, sound generation, and many independent companies within those areas. There are also several companies involved in coding, including coding assistants and vibe coders. Some of these are relatively new, while others have been around for 5, 7, or 8 years. They may not have started as AI-focused but have quickly adapted to growth in the AI sector. We notice a mix of companies that contribute to different parts of the AI stack, including those focused on infrastructure and applications filled with AI. Overall, it represents a diverse segment of the industry.

Our next question comes from the line of Mark Murphy of JPMorgan.

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 if, 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.

Yes. I mean, this is without getting to the detail of any specific customer like this is the motion is always the same; customers grow, they commit to more, they get better prices. So you see, like, again, talking about customers in general, you see both usage, drops in revenue as customers renew and get higher commit and a better price and then usually growth after that for those customers. That's the motion that we've had. We have about 30,000 customers so far.

Okay. What you are asking is whether the improved economics refers to where things will stand in about 12 months, is that correct?

Better economics mean that if you commit today, you will receive a more favorable price. We operate on a usage model where we charge customers monthly based on their usage at an agreed price. If you have better economics and your usage remains consistent from month to month, you will pay less, even though the overall trend of our business is increased consumption.

And then as a quick follow-up, Olivier, the acceleration that you saw in the security growth is pretty noticeable too. We recall, I think about 6 months ago, you had ramped up and engaged a lot more channel partners, which is a key ingredient to growing the 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 tracers?

Look, I think it's a number of things. Definitely, we've been investing in the channel, and that's certainly helpful to do the security business as a whole. The big win we mentioned on security that we mentioned a couple of wins in Cloud SIEM. These tends to be more related to product maturity. The strength of our underlying platform, especially when it comes to technology like Flex Logs, for example. And the fact also that we've been learning how to properly go to market for security. I think we see things clicking in a way that is exciting.

Our next question comes from the line of Fatima Boolani of Citi.

Oli, I'll start with you and I 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? And I'm very simplistically thinking about the core nature and value proposition of the On-Call product intelligently routing, requests for remediation, right? So how do you just broader advancements in AI help beef up and/or detract your ability to monetize this product? And then just a follow-up for David, please.

If you take a step back, we introduced On-Call to take charge of the entire incident resolution process. Previously, we were focused on detecting incidents and sending alerts, but we were not involved in the resolution phase. Customers were spending time analyzing data to figure out what was happening. Our goal was to manage the entire process. We believed that with AI, particularly, we could achieve capabilities throughout the whole cycle that wouldn’t be possible otherwise. Currently, this approach resonates with customers as they begin to adopt the product. For instance, we have some notable customers, such as one with 5,000 seats for On-Call, which is very promising. Looking ahead, there are numerous additional features we can develop for this product. By detecting and notifying about incidents, we can explore options like predicting incidents and alerting users in advance, rerouting traffic early, or advising users on potential fixes before an incident occurs. We’re actively working on these possibilities. If you consider the various product announcements we've made, such as Bits AI, SRE, or our time-series forecasting model, they collectively paint an intriguing picture of our future capabilities. We're excited about this, and our customers share that enthusiasm, which contributes to the success of these products.

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 installed 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?

Yes, you've nailed it. It's a trailing 12 months, and 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. So if that continues as you go into a trailing 12-month metric, you see a directional movement.

Our next question comes from the line of Eric Heath of KeyBanc.

Oli, David, Bits AI seems 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. So I'm just curious how you think about the agentic opportunity with Bits AI. How meaningful this can be for 2026 as a differentiator versus competition and also as a revenue contributor?

Yes, it's very exciting. The feedback we are receiving is positive. We've collected numerous customer quotes similar to the one I mentioned, which is very encouraging. Customers have started purchasing and engaging with it to demonstrate value and ensure we have the right product mix. We believe this is a high-quality offering that we can monetize. Regarding the impact for next year, I'm uncertain whether the significant effect will stem from our pricing of Bits AI itself or from the benefits it brings to the broader platform. This raises broader questions about packaging and AI monetization. It's important to note that our product operates on a usage-based model. Any increase in usage and customer adoption is beneficial and highly monetizable. We know that this is a differentiator and performs significantly better than anything else we've encountered in the market, and we are fully committed to its development. Multiple teams are focused on enhancing Bits AI SREs to ensure it not only identifies issues but also resolves them. We are also expanding its capability by training it on diverse data types and sources, even from systems that are not fully operational, to integrate better with other systems our customers use. We are aggressively advancing Bits AI SRE, and it is gaining strong traction in the market.

Our next question comes from the line of Gray Powell of BTIG.

Congratulations on the great results. So maybe just like 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. So I guess my question is, what surprised you the most this year? And then just how do you feel about the sustainability of those drivers as you look forward?

I apologize for exceeding expectations with our results. We may do it again, but we'll have to see. The biggest surprise has been that AI adoption has grown more rapidly than we anticipated at the start of the year. We've observed this trend across our AI segment. Additionally, some of our new products and the changes we've made to our go-to-market strategy have yielded results sooner than expected. Overall, we experienced faster growth in the leading part of our business driven by AI, while the slower-growing, more traditional segment also accelerated, contributing to our current position.

And I'd add that we have a good demand environment and 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 in investment, and we're seeing those investments pay off.

Our next question comes from the line of Koji Ikeda of Bank of America Securities.

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 broad-based? What I'm trying to get out here is I'm really trying to understand more the durability of this growth reflection.

So it is broad-based. I think, again, this 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, an acceleration for the hyperscalers in general. A lot of that is GPU related, but the growth we’re seeing here and the exception we’re seeing here is largely not GPU-related. It's a little bit of it, but not a ton of it. So 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, and this is largely the same go-to-market organization that we have a few segments. We've been doing well executing there. I think we've invested quite a bit in product, and we keep doing it, and we see the results of that.

I want to add that it's across the customer base, both enterprise and small to medium businesses. When we examine the situation, it's not solely an AI-driven phenomenon. Even without the AI companies, we still observe a growing demand cycle in small to medium businesses. Unlike previous periods, this trend spans all spending ranges. We are not witnessing a division between larger and smaller spenders; instead, there is a widespread trend of enhanced demand across various spending categories.

And 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...

Our next question comes from the line of Ittai Kidron of Oppenheimer & Co.

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've 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? And as you approach '26 years, do you anticipate any material changes in the comp structure just given the breadth of product and the list of opportunities, how do you get people focused?

We are optimistic about our overall sales productivity. The general principle is that growth comes from scaling capacity and maintaining productivity, which can be challenging to achieve simultaneously. If you aim to grow significantly, scaling is essential because improving productivity alone won't suffice. We have been successfully scaling our operations. Regarding our compensation and management strategies, we continually adjust them to ensure our sales team remains focused. Our business thrives on a land-and-expand model, primarily growing with existing customers. This presents a challenge as there is often more effort required to gain revenue from smaller or new customers compared to established ones. Many adjustments we made to our compensation plans are aimed at directing attention towards these newer customers and rewarding efforts that will yield long-term growth. We implemented several internal changes this year, and we are beginning to see positive results. Additionally, we are making strides with new go-to-market strategies, particularly by establishing multiyear plans to pursue larger, more challenging customers. Landing these customers can take longer than a year, but our compensation plan typically operates on a one-year cycle, which doesn't incentivize pursuing them effectively. To address this, we created special plans for certain companies, and we are already seeing promising outcomes from this approach.

Our next question comes from the line of Andrew Sherman of TD Cowen.

Great. Congrats. I know you have a team focused on the Fortune 500, where there's still a lot of white space for you. Curious to hear how the team is ramping to productivity. Did that help drive some of the strong new logo bookings, and can this contribute even more next year?

Yes. I mean, look, the team is not new, right? I mean, we've been focusing on that for many years, and we're 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 that there’s the right amount of sales attention and reward for the landing of a customer even if it's for a small amount, and I think we've done well. Again, we can comment on that again after the next quarter when we have a full year of our new clients that have been validated. So far, we feel very good about it.

Our next question comes from the line of Alex Zukin of Wolfe Research.

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?

We operate a consumption-based business, and the most challenging aspect is predicting the future of consumption. However, we are optimistic about the mid and long term outlook. There are fluctuations on a monthly or quarterly basis, which can be difficult to gauge. Historically, we have maintained confidence in the steady progress of digital transformation and cloud migration. While there may be some brief slowdowns, we believe that momentum tends to pick up again, and we anticipate this trend continuing for a considerable time.

Okay. And then maybe, David, for you, look, gross profit dollar acceleration while you're seeing your largest customer 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.

Yes, there are a couple of things. I think we've mentioned that we've been focused and have focused over 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 of customers coming in, it's smaller, so that balance there. In terms of the future, I'll repeat what we've always said: that we've been running the company with a gross margin plus or minus 80%, we've given that range and not changed it, and we watch it. It gives us mixed signals in terms of efficiency, how we're operating; it gives us good signals in pricing, things like that, and I wouldn't change the comments we made over many years about looking at that and then developing operations and strategies around that.

Our next question comes from the line of Ryan MacWilliams of Wells Fargo.

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?

No. I mean we don't provide that kind of information on individual customers. We're 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 we talk when we work on the term and the volume-based pricing, but we don't give guidance like that on individual customers.

Our next question comes from the line of Mike Cikos of Needham.

I just wanted to come back to it, Oli, for the non-AI native strength, I know we've touched on this a number of times, whether it's the 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? And then I have a follow-up for David.

I don't think there's much more to explain. It might seem dull, but it's how we've been growing for the past 15 years. I would describe it as typical.

I think it's across the customer base, enterprise and SMB. And when we look at it, it's not just an AI SMB. If you remove the AI companies, you still see a strengthening SMB demand cycle going on. Unlike in previous periods, it is also 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.

Our next question comes from the line of Karl Keirstead of UBS.

Okay, great. 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 1-year deal or multiyear? 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 1-year deal, does the entirety of that contract contribute to the sequential CRPO performance in the quarter? So 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's target or because a lot of what Oracle is doing with the AI native is training clusters; perhaps that near-term opportunity is more limited.

Yes. Regarding the first point, we have provided numerous examples of our strategy, which our customers adhere to. One aspect is that we finalize our annual plus commitments. While we are not discussing individual contracts, the process would align with the typical progression of other contract types. That is our approach.

Yes. We've created an OCI integration, and we're noticing increased demand from customers for OCI. However, many of the projects we see are highly customized for specific clients, which means they aren't typical cloud solutions. As such, the current opportunity is more limited. If several companies begin adopting this, it could become a significant commercial opportunity. We're focused on going where our customers are.

I think you mentioned about the RPO. I think in this case, we've mentioned this current and the total is roughly the same, and 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.

Our next question comes from the line of Jake Roberge of William Blair.

Yes. Just on the recent go-to-market investments. Obviously, it seems like there's been a lot of traction thus far with those. So 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.

Yes, definitely. There are several initiatives we didn't pursue this year that we will definitely address next year. As we are currently in Q4, we are in the midst of planning for the upcoming year. We will continue to scale what is effective, discontinue initiatives that have not yielded clear results, and explore additional opportunities. Building a go-to-market strategy is somewhat similar to software development; it involves experimenting with data to identify what is successful and what is not, and then developing the required systems.

That's helpful. And then just on the new Bits 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 compares 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?

The Bits AI Agent has a significant growth potential for our customers. We've observed that when we set it up, it runs on their alert systems. For instance, during an outage, they still need to set up a bridge with about 20 people spending 2 hours trying to figure out what went wrong. Meanwhile, they can go to Datadog and discover that just three minutes into the outage, their system reached the same conclusion we did after 2 hours with the team on the call. This realization is quite enlightening for customers. We receive numerous positive quotes about it. However, there’s more we need to accomplish; customers often ask if it can make certain fixes or support additional systems that we currently don't cover. We have a comprehensive roadmap ahead and are fully committed to it. Additionally, we've released a security agent that assesses vulnerabilities and analyzes security signals, which provides insights into potential benign issues versus real problems. The feedback for this has been overwhelmingly positive, and this capability has been instrumental in securing significant deals for our Cloud SIEM products. The efficiency of our SIEM running on observability data and the Flex Log saves customers substantial time by automating the resolution of 90% of issues, making it highly appealing to them.

And 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 we have quite a lot going on in Q4, whether it's on the planning side, the product building side, or on the sales side where we have a really exciting pipeline. I want to thank the team for the hard work there. I'm looking forward to meeting many of our existing and new customers at AWS re:Invent in a few weeks, and I'll see you all there. Thank you for participating in today's conference. This does conclude the program. You may now disconnect.