Datadog, Inc. Q2 FY2025 Earnings Call

Thank you, Didi. Good morning, and thank you for joining us to review Datadog's Second 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 third 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 March 31, 2025. Additional information will be made available through our upcoming Form 10-Q for the fiscal quarter ended June 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 you all for joining us this morning to go through our results for Q2. Let me begin with this quarter's business drivers. Overall, we saw trends for usage growth from existing customers in Q2 that were higher than our expectations. We experienced strong growth in our AI native cohort. The number of AI native customers is growing meaningfully with us as they see rapid usage growth with their products. Meanwhile, we saw consistent and steady usage growth in the rest of the business. We continue to see the overall demand environment as solid with an ongoing healthy pace of cloud migration and digital transformation, and 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 Q2 financial performance and key metrics, revenue was $827 million, an increase of 28% year-over-year and above the high end of our guidance range. We ended Q2 with about 31,400 customers, up from about 28,700 a year ago. This includes about 150 new customers from our EPO and MetaPlan acquisitions. We ended Q2 with about 3,850 customers with an ARR of $100,000 or more, up from about 3,390 a year ago, and these customers generated about 89% of our ARR, and we generated free cash flow of $165 million with a free cash flow margin of 20%. Turning to platform adoption. Our platform strategy continues to resonate in the market. At the end of Q2, 83% of customers were using two or more products, the same as last year, 52% of customers were using four or more products, up from 49% a year ago, 29% of our customers were using six or more products, up from 25% a year ago, and 14% of our customers were using eight or more products, up from 11% a year ago. So our customers continue to adopt more products, including our security offerings. As a reminder, our security customers can identify and manage vulnerabilities, with code security, cloud security, and a sensitive data scanner, and they can detect and protect from attacks with app and API protection, workload protection, and Cloud SIEM. We are pleased that our security suite of products now generates over $100 million in ARR and is growing in the mid-40s percent year-over-year. While we are pleased to achieve this milestone, we're still just getting started in selling customer products in this area with new innovations such as our Bits AI security and noise. Moving on to R&D. We held our DASH user conference in June, where we announced over 125 exciting new products and features for our users. So let's go through some of the announcements. First, we launched fully autonomous AI agents, including Bits AI SRE Agent to investigate alerts and coordinate incident response, Bits AI Dev Agent, an AI-powered coding assistant to proactively fix production issues, and Bits AI Security Analyst to triage Datadog Cloud SIEM signals. To further accelerate our users' incident response, we announced an AI voice agent for incident response, so users can quickly get up to speed and start taking action on their phones. We also announced handoff notifications that make it easy to jump straight into the relevant context and quickly communicate with our responders and status pages to enable automatic updates for customers who undergo an incident. Second, we delivered a series of products to help customers ship better software with confidence. With the Datadog internal developer portal, developers can ship better and faster by gaining a real-time view into their software systems and APIs with the software catalog by provisioning infrastructure, scaffolding new services, and managing code changes and deployments with self-service actions and by following engineering readiness standards with scorecards. We launched a Datadog MCP server to enable AI agents to access telemetry from Datadog and to act as a bridge between Datadog and MCP compatible AI agents like OpenAI Codex, Cursor, and Claude Code by Anthropic. We work together with OpenAI to integrate our MCP server within the OpenAI Codex CLI, and the Datadog Cursor extension now gives developers access to Datadog tools and observability data directly within the Cursor IDE. Third, we are reimagining observability to meet our customers' increasingly complex needs. Our APM latency Investigator formulates and explores hypotheses in the background, helping teams to quickly isolate root causes and understand impact without combing through large amounts of data. Proactive app recommendations help users stay ahead of growing system complexity by analyzing APM data to detect issues and propose fixes before they become problems. We announced a Flex Frozen tier, so customers can keep logs in fully managed storage for up to 7 years and be able to search without data movement or rehydration. Archived search now enables teams to query archive logs directly in cloud storage like Amazon S3 bucket or in the Flex Frozen tier, and Datadog now supports advanced data analysis features within notebooks. Fourth, our security products cover new AI attack vectors across the application, model, and data layers. At the AI data layer, sensitive data scanner can now prevent the leakage of sensitive data and training data as well as LLM prompts and responses. At the model layer, we help secure against supply chain attacks in open-source models and prevent model hijacking attacks. At the application layer, we help prevent prompt injection attacks and data poisoning in real-time. And finally, we showcased our new end-to-end AI and data observability capabilities. Engineers and machine learning teams can use GPU monitoring to gain visibility into GPU fleets across cloud, on-prem, and GPU-as-a-service platforms such as CoreWeave and Lambda Labs. With the AI Agent console, enterprises can monitor the behavior and interactions of any AI agent used by their teams. We now offer LLM observability experiments to help understand how changes to prompts, models, or AI providers influence application outcomes. We added a new agentic flows visualization to LLM Observability to capture and understand the decision path of AI agents. And last but not least, accelerated by our recent acquisitions of MetaPlan, Datadog now offers a complete approach to data observability across the entire data lifecycle from iteration to transformation to downstream usage. So we continue to relentlessly innovate to solve more problems for our customers. In doing so, we are being rightfully recognized by independent research, and we are pleased that for the fifth year in a row, Datadog has been named as a leader in the 2025 Gartner Magic Quadrant for Observability platforms. We believe that this validates our approach to deliver a unified platform, which breaks down silos across teams. Now let's move on to sales and marketing. We had a number of great new logo wins and customer expansions this quarter. So let's go through a few of those. First, we signed a 7-figure annualized expansion in a 3-year contract worth more than $60 million with one of the world's largest banks. This company believes getting to the cloud is essential, so they can use AI on their extremely rich dataset to improve how they manage risk and serve their customers. They are using Datadog as their strategic cloud observability platform, and they continue to migrate more applications to the cloud. This customer is expanding to 21 Datadog products with thousands of users who log into the Datadog platform every month. Next, we signed a 7-figure expansion to an 8-figure annualized contract with a leading U.S. insurance company. Datadog is supporting this customer's efforts to consolidate observability tools and expand their cloud-based products. By adopting Datadog, they are experiencing fewer and less severe incidents, with estimated savings of over $9 million per year in incident response costs, and improving more than 100,000 customer transactions that would otherwise be impacted every year. With this expansion, this customer will adopt 19 Datadog products and will consolidate a couple of dozen tools across multiple business units. Next, we signed a nearly 7-figure annualized expansion with a leading American media conglomerate. This customer has about 100 observability tools across more than 300 business units, and this tool fragmentation has resulted in inefficiencies, extra costs, and lost engineering time. They are expanding to 21 Datadog products, including all of our security products and replacing their paging solution with Datadog On-Call and Incident Management. Next, we landed a 7-figure annualized deal with leading Brazilian e-commerce companies. This customer's previous observability vendor was unable to support them as they moved to newer software platforms and modern cloud infrastructure. By replacing this tool with Datadog, the company was able to gain full visibility into its cloud tech SaaS and saw significant improvements in application stability and incident resolution times. This customer will start with 7 Datadog products, including Flex Logs. Next, we landed a 7-figure annualized deal with the delivery app of a major American retailer. This customer found our RUM and error tracking products to be immediately valuable, finding an issue on the first day of their Datadog trial that they hadn't identified after months of searching with their old tool. By adopting Datadog with 7 products to start, this customer will consolidate half a dozen tools while meeting their PCI compliance requirements. Finally, we welcome back a leading U.S. mortgage company in a nearly 7-figure annualized deal. This customer has moved to using a dozen open-source disconnected tools, which led to fragmented visibility, fatigue, and poor customer experience. In returning to Datadog, they plan to adopt 6 products, including replacing their paging system with Datadog On-Call. And that's it for another productive quarter from our go-to-market teams who are now very hard at work on a busy Q3. 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. As we think about AI, we are incredibly excited about our opportunities. First, AI is a tailwind for Datadog as increased cloud consumption drives more usage of our platform. Today, we see this primarily in our AI native group of customers who are monitoring their cloud-native applications with us. There are hundreds of customers in this group. They include more than a dozen that are spending over $1 million a year with us and more than 80 who are spending more than $100,000, and they include 8 of the top 10 leading AI companies. 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, and we continue to see rising customer interest for next-gen AI observability and analysis. Today, over 4,500 customers use one or more Datadog AI integrations. Second, next-gen AI introduces new complexity and new observability challenges. Our AI observability products help our customers gain visibility and deploy with confidence across their entire AI stack, including GPU monitoring, LLM observability, AI agent observability, and data observability, and we will, of course, keep innovating as the AI landscape develops further. Third, we are incorporating AI into the Datadog platform to deliver more value to our customers. As I discussed earlier, we launched Bits AI SRE Agent, Dev Agent, and Security Agent. We are seeing very good results with those with more improvements and new capabilities to come. Finally, as a SaaS platform focused on our customers' critical workflows, we have a large volume of rich clean and detailed data, which allows us to conduct groundbreaking research. A great example of that is our Toto, foundational model for time series forecasting, which shows state-of-the-art performance on all benchmarks, even going well beyond specialized observability use cases, and you should expect to see more from us on that front in the future as well as taking novel research approaches and models straight into our products to improve customer outcomes. So we are extremely excited about our progress so far against what we expect to be a generational growth opportunity. In other words, we're just getting started. And with that, I will turn it over to our CFO. David?

Thanks, Olivier. Q2 revenue was $827 million, up 28% year-over-year and up 9% quarter-over-quarter. Now to dive into some of the drivers of this Q2 revenue growth. First, overall, we saw trends for usage growth from existing customers in Q2 that were higher than our expectations. This included strong growth in our AI native cohort as well as usage growth from the rest of the business that was consistent with recent quarters amidst a healthy and steady cloud migration environment. We saw a continued rise in contribution from AI native customers in the quarter who represented about 11% of Q2 revenues, up from 8% of revenues in the last quarter and about 4% of revenues in the year-ago quarter. The AI native customers contributed about 10 points of year-over-year revenue growth in Q2 versus about 6 points last quarter and about 2 points in the year-ago quarter. Now as previously discussed, we do see revenue concentration in this cohort in recent quarters. But if we look at our revenue without the largest customer in the AI native cohort, our year-over-year revenue growth in Q2 was stable relative to Q1. We remain mindful that we may see volatility in our revenue growth on the backdrop of long-term volume growth from this cohort as customers renew with us on different terms and as they may choose to optimize cloud and observability usage over time. As you heard from Oli, we continue to believe that adoption of AI will benefit Datadog in the long term, and we believe that the growth of this AI native customer group is an indication of the opportunity to come as AI is adopted more broadly, and customers outside the AI native group begin to operate AI workloads in production. Now regarding usage growth by customer segment. In Q2, our year-over-year usage growth was fairly similar across segments relative to previous quarters as SMB and mid-market usage growth improved in Q2, while enterprise customer usage growth remained roughly stable. Note that we are excluding the AI native cohort for the purposes of this commentary, and as a reminder, we define enterprise as customers with 5,000 or more employees, mid-market as customers with 1,000 to 5,000 employees and SMB as customers with less than 1,000 employees. Regarding our retention metrics, our 12-month trailing net retention percentage was about 120, higher than the high 110s last quarter, and our trailing 12-month gross revenue retention percentage remains in the mid- to high 90s. Now moving on to our financial results. First, billings were $852 million, up 20% year-over-year, and remaining performance obligations, or RPO, was $2.43 billion, up 35% year-over-year. Our current RPO growth was in the low 30s year-over-year, and our RPO duration was up slightly year-over-year. As previously mentioned, we continue to believe that revenue is a better indication of our business trends than billings and RPO, as those can fluctuate relative to revenue based on the timing of invoicing and the duration of customer contracts. 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 $669 million for a gross margin of 80.9%. This compares to a gross margin of 80.3% last quarter and 82.1% in the year-ago quarter. As we've discussed in the last call, we saw an increasing impact of our engineers' cost savings efforts throughout this quarter as they delivered on cloud efficiency projects. And we are continuing our focus on cloud efficiency and believe that we have further opportunity for gross margin improvement in the second half of the year. Our Q2 OpEx grew 30% year-over-year, up from 29% last quarter. As we've communicated over the past year, we plan to grow our investments to pursue our long-term growth opportunities, and this OpEx growth is an indication of our execution on our hiring plans. Q2 operating income was $164 million for a 20% operating margin compared to 22% last quarter and 24% in the year-ago quarter. Within that, as we've noted, we held our DASH user conference in June, and as expected, the event cost $13 million. We also experienced a rising impact from the weaker dollar and absorbed $6 million of negative FX impact during Q2. Excluding those expenses, operating income would have been 22% in Q2 or 200 basis points higher, and now turning to the balance sheet and cash flow statements. We ended the quarter with $3.9 billion in cash, cash equivalents, and marketable securities, and our cash flow from operations was $200 million in the quarter. After taking into consideration capital expenditures and capitalized software, free cash flow was $165 million for a free cash flow margin of 20%, and now for our outlook for the third quarter and the remainder of fiscal 2025. First, our guidance philosophy overall remains unchanged. As a reminder, we base our guidance on recent trends observed and apply conservatism on these growth trends. For the third quarter, we expect revenues to be in the range of $847 million to $851 million, which represents a 23% year-over-year growth. Non-GAAP operating income is expected to be in the range of $176 million to $180 million, which implies an operating margin of 21%, and non-GAAP net income per share is expected to be $0.44 to $0.46 per share based on approximately 364 million weighted average diluted shares outstanding. For fiscal 2025, we expect revenue to be in the range of $3.312 billion to $3.322 billion, which represents a 23% to 24% year-over-year growth. Non-GAAP operating income is expected to be in the range of $684 million to $694 million, which implies an operating margin of 21%, and non-GAAP net income per share is expected to be in the range of $1.80 to $1.83 per share based on approximately $364 million average diluted shares. Some additional notes on our guidance. We expect net interest and other income for fiscal 2025 to be approximately $150 million, and due to the impact of the recent federal tax legislation, we now expect cash taxes for 2025 to be about $10 million to $20 million. We continue to apply a 21% non-GAAP tax rate for 2025 and going forward, and finally, we expect capital expenditures and capitalized software together to be 4% to 5% of revenues in fiscal year 2025. To summarize, we are pleased with our execution in Q2, including the many products and features we launched at DASH. 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 all Datadogs worldwide for their efforts. And with that, we'll open the call for questions. Operator, let's begin our Q&A.

Our first question comes from Raimo Lenschow of Barclays.

Perfect. Two quick questions from me. Olivier, like you talked about the AI contribution and slowly broadening out. How should we think about it in terms of when this goes much broader into inference, et cetera? So does that everyone like Barclays, JPMorgan, et cetera, they all kind of need to do more around observability because they're going to do more inference, et cetera? So in a way, like OpenAI, et cetera, is just setting the scene for the future? And what do you think about the market opportunity there? And then, David, in the second half of last year, you hired a lot of extra sales guys. Can you talk a little bit about that ramp and where they are in their productivity curve?

Yes. On the AI opportunity, so there's really multiple layers to it. The first layer is largely what we see today, which is companies that are running their inference stack and the application around it in cloud environments. So that's the case of the model makers or if you think of the companies that are doing coding agents, things like that. That is what we see today, and it looks a lot like normal compute. So you have normal machine CPUs, some GPUs, quite a few other components, databases, web servers, things like that. So that's the bulk of what we see today. And there's going to be more of it as the AI applications come into production. There are more specialized inference workloads and even training workloads in some situations that rely on instrumenting GPUs. And for that, we have a new product out there that does GPU monitoring that we announced at DASH. But all that I would call the infrastructure layer of AI. Then on top of that, there's new problems in terms of understanding what the applications themselves are doing, and the applications are largely nondeterministic anymore. They are either run by a model that is nondeterministic by nature or they run in code that was not as carefully written as it used to be. It's not completely written by humans, just largely written by AI agents, and as a result, you also need to spend a lot more time understanding how that code is working, and that largely happens in production. So that's a brand new area of observability, which is how do you deal with applications that have not been completely defined in development and that have to be evaluated in production. And what we think is the whole market is going there, not just the AI natives, the AI natives are definitely doing that today, both applications are running on models and code that has been largely written by agents, but the rest of the market is going there, and the best proof point you see of that is the very, very broad adoption today, both of the API gated AI models and of the coding agents, which you see in every single large enterprise today.

Yes. And as to sales capacity, we have been successful in increasing both our number of salespeople and our ramp sales capacity. We started that, as you said, in the last part of 2025, and we are seeing evidence of that through our new logo production and our pipeline. We need to, as we talked about previously, go through the ramping of that, but in looking at the size and productivity and performance, we see some good signs that that core capacity is becoming productive.

Our next question comes from Sanjit Singh of Morgan Stanley.

Congratulations on the outstanding results this quarter. David, as I review the guidance, it appears to be one of the most impressive forecasts I've observed coming out of Q2 in recent years. Considering your comments about the AI native cohort potentially facing volatility, I’m trying to reconcile that with such strong guidance. Is it reasonable to assume that you’re not currently experiencing any risks from this cohort, and that any potential issues might arise later? The guidance looks very solid and doesn’t seem to reflect significant volatility from the AI native cohort at this point.

Yes. We provided metrics showing that the AI cohort is growing rapidly and we are gaining significant market share in that area. In terms of how we factor this into our guidance, we understand there may be volatility in usage or when negotiating unit rates. Consequently, we take a conservative approach to our performance projections for the rest of the year. Although we haven't seen this volatility reflected in our current growth metrics, our past experiences with cloud natives teach us that fluctuations can occur, and we want to ensure this is considered in our guidance.

Perfect. And then, Olivier, with the new security disclosures, congrats on crossing the $100 million threshold. Is there any sort of change in the buying behavior? There's been consolidation in the industry. You guys have been advancing your portfolio quite significantly. You guys have fully autonomous security agents. What's your prospect for this pool of the business, this part of the business to drive growth for the balance of the year and going into 2026?

Yes, we have a strong product lineup, including three different products. Some of these are starting to gain significant traction with customers. Currently, we are seeing broad adoption in security, with a good number of customers, including several spending over $1 million on our security solutions. We are pleased with this progress. However, we haven't yet achieved standardized adoption across large enterprises. This is our next focus in security, which will involve both product enhancements and adjustments to our go-to-market strategy to improve enterprise-wide sales, a strategy we haven't utilized extensively in the past. We believe we have laid a strong foundation with our products, but there is still substantial work ahead and many opportunities before us. That's why we are prioritizing this area.

And our next question comes from Kash Rangan of Goldman Sachs.

This is Matt Martino on for Kash Rangan. David, you called out enterprise consumption volatility last quarter. It sounds like that may have been consistent this time around while SMB continues to improve. So could you perhaps characterize any discernible trends between these two customer demographics? What went right relative to your expectations heading into 2Q and really how that informs your second half guide?

Yes. I think broadly, we're calling out that the usage trends across the segments were roughly consistent with the previous quarters. We said we did see some more concentrated. This is not a comment about AI. This is a comment about enterprise take, less consumption relative to a spike, but we saw that stabilize, and we've seen small, but gradual improvement of the SMB as a result of their usage of our products.

And our next question comes from Mark Murphy of JPMorgan.

Congrats. So Olivier, I actually wanted to ask you about Toto and BOOM, those announcements. It looks like you're bringing very serious AI research to a space where it is applicable and opening it up very broadly, the size of the dataset is vast. I'm curious what type of response do you expect to see here? And just help us understand maybe how that can sustain growth in future years? And then I have a quick follow-up for David.

We believe there is significant potential in automation through autonomous AI agents. We have outlined three main areas of focus so far. The first is Site Reliability Engineering (SRE), which involves managing alerts, investigating them, and resolving issues. The second area is coding, where we address problems identified in production and validate our fixes. The third area pertains to security, where we independently analyze security signals, alleviating that responsibility from our customers. There is much more to explore in this domain, and the success of our efforts will heavily rely on strong research. This is why we established a dedicated research team and introduced research models with Openwave. The next step is to integrate these models into our products, which is currently in progress. We are excited about the opportunities ahead and are pleased with the promising outcomes from our initial release. Our research output has produced a state-of-the-art model that surpasses all other models in a competitive category, particularly in time series forecasting, which has broad applications across various fields. This indicates that we can excel at the highest levels, marking a positive outlook for the future of AI automation and AI agents.

Okay. And then, David, we keep pointing out that Datadog is one of the only software companies that's investing seriously in headcount growth, and it feels like that is paying top-line dividends pretty tremendously today. We noticed the R&D spending is up noticeably in Q2. Just wondering what are the mechanics that are driving that on the R&D line? And then the flip side is what's allowing you to guide operating income so much higher in Q3 than you had guided that for Q2?

Yes. In Research and Development, we had an aggressive investment plan that we’ve successfully executed. Thanks to our recruitment team, we brought in the right talent earlier this year. However, there are some factors related to foreign exchange that have an impact, particularly given our significant R&D center in Paris. Overall, though, the focus has been on execution and recruitment. In the second quarter, we saw a 36% increase in operating income, driven in part by the timing of DASH, which contributed $13 million, along with foreign exchange factors. We have a clear understanding of the drivers in R&D, though some operating expenses do exhibit seasonal patterns.

The one thing I would add, which is that we also are spending more on AI training and inference in R&D if you compare to past years in R&D, and the output of that is things such as Toto or the next versions of it that we're training right now and experiments we're running to train agents, run simulations to train agents and things like that. You shouldn't expect the overall picture of our R&D investment to change in the future, although I think we expect the same envelope to be what we use moving forward.

I would like to highlight our R&D team and FinOps for the commitment we made last quarter to optimize our cloud usage. This focus impacts our gross margin, and as you know, we utilize many of our applications internally. We achieved considerable success in Q2 with this approach and plan to continue enhancing our cloud efficiency, which will influence our margins and operational spending growth rates as we progress through the year.

And our next question comes from Koji Ikeda of Bank of America.

We all see that the second quarter was really, really strong. Guidance for 2025 looks really, really great. And so I wanted to ask you about contract visibility. How are you feeling about contract visibility, specifically with your large AI native customers? I have to imagine you're very close to these customers and having lots of conversations with them. And so I know there is some concern about there. And David, you mentioned potential volatility. So I really want to ask about how you're feeling about contract visibility.

We can't discuss any specific customers, as each one has their own individual journey. However, we do have strong product engagement from our top customers overall. Our goal is to make Datadog the best platform for companies of all sizes, even those experiencing unprecedented growth. It's also important to note that we have extremely high customer retention. For most customers, it doesn't make sense for them to build their own solutions. We've seen customers who previously left to develop their own systems return to us, and we mentioned one during the call. This gives us confidence in our business forecasts for the mid- to long-term. Typically, when we renegotiate with customers and they increase their volume, we might experience short-term revenue drops, but ultimately, this leads to long-term growth, which has always been our approach.

I have a follow-up on security. It's great to hear about the milestones, like reaching $100 million and a 40% growth. Considering your product offerings, how do you plan to expand those capabilities moving forward? Are you leaning towards organic growth, inorganic growth, or both? Also, could you provide an update on your M&A philosophy? In short, are you open to significantly scaling up to enhance your security strategy?

We are exploring various aspects of security, which is a field with many companies and product areas. We already cover a significant range and have the potential to expand further. This sector requires us to address essential foundational features while also investing in future innovations, particularly as AI is transforming the landscape. There is substantial work ahead, and you can expect us to pursue more mergers and acquisitions in this area, just as we do across other sectors of our business, because there are many valuable assets and growth opportunities available.

And our next question comes from Karl Keirstead of UBS.

Okay. Great. Maybe I'll direct this to David and link the AI native exposure to margins. So David, now that the AI natives are 11% of Datadog's revenue mix, I think it's fair to ask whether the revenues from that cohort are coming at similar margins as the rest of the business? Or do you think that this could be even short term, a modest source of margin pressure?

Yes. I would say like we talked about last quarter, this isn't about the AI and margins, the AI cohort versus non-AI cohorts. We price based on volume and on term. So to the extent you would have an AI customer who's doing much the same things as our other customers in the use of the product, has similar volumes and similar terms to the non-AI, it would be similar margins. To the extent that we have a larger customer in there, given our price grids, that customer would get a better discount. That's the way we've always priced. So it really is related to customer size rather than AI native or non-AI native.

And with a bit of it in commercial, so we did see, as we mentioned last quarter, we were seeing gross margins going down a little bit further than we would like them to. So what happened is we task our engineering teams with optimizing the cloud usage, which goes across all of our customer base. What we did is we turned to our own product, we turned to our cloud cost management product and our profiling product largely, and then we, in a matter of months, will really turn up substantial improvements, savings on our bills and improvements in performance and efficiency of our systems, while we're still shipping new features, and that's something that we're working to bring to all of our customers so they can get the same effect and see their margins go up as well.

Got it. And maybe the natural follow-up there is, David, you mentioned that you're optimistic about gross margins in the second half. Is that because of what Olivier just mentioned? Or are there some other drivers you have in mind?

No, it's because of what Olivier mentioned. So we said we were engaging in these efforts. And as we were more successful in the quarter, we will be carrying that run rate forward, which wasn't fully in Q2 as well as using what Olivier mentioned, using cloud cost management and our projects to have further opportunities going forward. So it's really about our progress and pace, which has been successful in our cloud efficiency going forward.

And our next question comes from Mike Cikos from Needham.

I wanted to return to the enterprise segment for Oli. From my perspective, we see the enterprise experiencing stable growth. Is it reasonable to compare traditional enterprises using CPUs with AI-focused companies that are increasing their investments in GPUs? Is it similar to 15 years ago when on-premises solutions continued to receive funding, but a larger share of the budget shifted toward the cloud? Is that a fair comparison when considering the behavior of these different customers and the direction Datadog is heading?

I’m not sure it can be stated this way because back then on-prem versus cloud typically involved different customers. Today, however, AI-native companies and enterprises are distinct entities. The primary distinction is that AI natives are experiencing rapid growth in their business and infrastructure, while enterprises are gradually transitioning from on-prem to cloud. This transition is more constrained by their capability to manage the migration rather than an overwhelming demand for their services. In our enterprise segment, we observe positive trends in bookings, new products, and new customers purchasing from us. However, the growth in usage is somewhat slower right now, which likely reflects their capacity to shift workloads and accelerate processes. Much of their focus is currently on determining which AI technologies to implement and how to bring AI applications into production. Overall, we find the growth rate to be stable and believe we will see increased growth from enterprise customers as they start deploying AI applications in the future.

Understood, and congrats on the security. I didn't want to leave hanging. I don't know if we got commentary on it, but could we please get an update on Flex Logs? I know it was a shining star if I go back a quarter ago, but just wanted to see how progress is tracking on the Flex Log side of the house.

All significant deals with enterprise customers now incorporate Flex Logs in some capacity, which resonates well, particularly when clients are looking to transition from traditional log solutions. We are focused on ensuring this migration is seamless for them and are investing in various initiatives in that area. Flex Logs are particularly appealing as they significantly improve the economics and predictability of observability costs, which is a key concern for data-heavy observability like logs.

And our next question comes from Jake Roberge of William Blair.

There's obviously been a lot of talk about AI natives around the business. I know you've talked about the potential for optimization for several quarters, but we continue to see really strong growth in that segment. So if you were to see optimization, when would you expect that to happen? And as you get a wider swath of customers in that AI native cohort, do you think you're at the place where you could actually digest an optimization by one or two of those customers?

Well, I mean, look, if I knew when it was going to happen, I would tell you. The nature of our customers is they grow, they have their own businesses to run. They have their own constraints. We're here to help them deliver their services, and that's what we work on every single day. Now every now and then, there's a renegotiation, a renewal on occasions for customers to figure out what they need to optimize and what they need to do for the future. But we never know whether it's going to happen this quarter, next quarter, in three quarters, next year, never. That's really hard to tell.

Okay. That's helpful, and then could you also talk about the uptake and feedback that you're getting for your own AI solutions like Bits AI, the new observability agents? And when do you think those could really start layering into the model?

Yes. So I mean the initial response to the AI agents is really pretty positive. So the AI actually works surprisingly well. I mean, if you think of how far the technology has grown in a number of a couple of years, and so right now, we're busy basically shipping it to as many customers as we can and enabling the customers with it, and that's a big area of focus in the business as well. I think it was developed by a fairly small team, the actual product that we ship, and now we're busy scaling that up as fast as we can so we can serve all those customers. That's the core focus of the business today. So the initial response is very positive. We've had customers purchase it pretty quickly in their trials, and so we feel very good about it.

And our next question comes from Brent Thill of Jefferies.

David, just on the quota-carrying rep capacity, I know you've been investing aggressively ahead of the curve. But when you think about 2025, are you accelerating that count based on the great results you've seen? Are you digesting that count given those reps are on board? Just give us a sense and flavor of what that quota rep count looks like through the rest of the year, and if you can shape the year how that looks versus '24.

Yes. We are executing the plan we initiated at the beginning of the year. We recognized that we had not invested enough in our go-to-market strategy and analyzed the opportunities available. I can say that we are successfully implementing that plan. Initially, the plan was slightly front-loaded due to our desire to capitalize on this opportunity, and we are continuing to execute. As we approach the end of the year, we will evaluate the metrics related to this and adjust our perspective on growth for next year.

Okay, Olivier, I'm curious about many CEOs either keeping their headcount the same or reducing it. We've seen Meta's headcount decrease over the past two years, Microsoft's headcount remains flat, and others like Palantir are planning to reduce their headcount while attempting to increase revenue tenfold. Do you think you can be more efficient with fewer employees, or do you believe that model doesn't apply to what you're observing with other software companies?

There is definitely a shift in spending on the engineering side. We're consuming more AI training inference, which is changing the balance between human tasks and those handled by GPUs. However, we are still completely limited by how much product we can produce. There are numerous opportunities in every direction we explore, whether in AI automation, security, new areas, improved observability, or experimentation. This gives us a strong return on investment in the additions we are making right now.

And our next question comes from Andrew DeGasperi of BNP Paribas.

First, on the ramp-up in terms of sales capacity, would you say that's been broad-based in terms of the productivity across both international and domestic?

As we talked about previously, we have a less developed international footprint, and so our growth rate internationally is running higher. We have markets we've talked about before like Brazil and India and parts of APJ and the Middle East that we have opportunities to grow our footprint. So we are executing in that way. We're doing a bottoms-up as always. We're looking at the accounts. We're looking at the TAM and we're looking at how much we're covering it. So that produces a result of a little more investment intensity internationally versus in North America, but there are lots of opportunities in North America as well.

That's helpful, and then on the enterprise side, I mean given some of these reps are obviously on the ground, should we expect the number of attach rates in terms of the 3 or 4 more products per customer sort of accelerate at this level? I know they've been ticking up about 1 point every quarter. Just wondering if that's something we should be seeing.

We generally anticipate that the trends we've observed in landing with our core products and then expanding will continue. As the platform has grown, we've typically landed with more products, and we expect these trends to persist in different regions.

It is generally easier to upsell an existing customer than to acquire a new one when you're in the field. Much of our efforts in territory management and collaborative planning with the sales team aim to ensure there are sufficient incentives for pursuing new customers. We strive to increase the number of new customers as well. There is a constant balance between directing the sales force towards upselling current customers and acquiring new ones.

And our next question comes from Patrick Colville of Scotiabank.

And I guess I just wanted to say before I ask my question, congrats on the S&P 500 Index inclusion. I mean that's a really nice milestone for you guys. Look, the question we get consistently from investors is on competition. I mean you referred to your views on competition kind of tangentially in other answers, but maybe more specifically, I mean, what are you seeing competitively in observability? And the one we get asked about a lot is versus Grafana and Chronosphere.

Yes, there has always been competition in this field. When I first raised funds for Datadog, I consistently heard that it was a crowded space. Throughout the company's history, we've faced not only established competitors but also a continuous influx of new entrants each year. There are always new companies and individuals developing innovations in observability, which is appealing for engineers. Generally speaking, the community landscape hasn't changed significantly over the past decade or so. Our strategy for success involves providing an integrated platform that addresses a wide array of problems for our customers from start to finish. We do not concentrate on just one aspect or data source that our customers may seek; instead, we aim to solve the complete issue comprehensively. In the long term, our success will come from innovation and an economic model that allows us to invest more in research and development, rapidly expand our product offerings, and adapt existing products effectively, outperforming our competitors in covering more related areas. This is why we believe we will succeed. None of the companies you mentioned are in a position to match that. As we wrap up, I want to express my gratitude to our customers for collaborating with us to launch these exciting new products. We had a significant agenda this year, and it was fantastic to meet our customers at DASH and see their positive reactions to our new offerings. We are also working closely with many of them on the adoption of these products and the developments expected in Q3 and Q4. Thank you, and I look forward to speaking with you next quarter.

This concludes today's conference call. Thank you for participating, and you may now disconnect.