Earnings call transcript: Datadog Q2 2025 beats forecasts, stock surges

Datadog Inc. (DDOG) reported stronger-than-expected earnings for the second quarter of 2025, with both earnings per share (EPS) and revenue surpassing analyst forecasts. The company posted an EPS of $0.46, exceeding the projected $0.41, marking a 12.2% surprise. Revenue reached $827 million, outpacing the anticipated $790.9 million. Following the announcement, Datadog’s stock rose 8.06% in pre-market trading, reflecting positive investor sentiment. According to InvestingPro data, the company maintains impressive gross profit margins of 80.15% and shows strong financial health with a "GOOD" overall rating. InvestingPro analysis suggests the stock is currently trading above its Fair Value.

Key Takeaways

• Datadog’s EPS and revenue beat analyst expectations.
• Stock price increased by over 8% in pre-market trading.
• The company continues to see strong growth in AI and cloud services.
• Q3 revenue guidance suggests continued robust growth.

Company Performance

Datadog demonstrated robust performance in Q2 2025, with a 28% year-over-year increase in revenue. The company ended the quarter with 31,400 customers, up from 28,700 a year ago. Notably, 3,850 customers contributed over $100,000 in annual recurring revenue (ARR), accounting for 89% of total ARR. Datadog’s focus on AI and cloud services continues to drive its growth, aligning with industry trends toward digital transformation. InvestingPro data reveals the company’s strong revenue CAGR of 49% over the past five years, with analysts expecting 20% growth in FY2025. InvestingPro subscribers have access to 13 additional key insights about Datadog’s growth potential and financial health.

Financial Highlights

• Revenue: $827 million, up 28% year-over-year.
• EPS: $0.46, exceeding the forecast of $0.41.
• Gross Margin: 80.9%.
• Free Cash Flow: $165 million, representing a 20% margin.
• Billings: $852 million, up 20% year-over-year.

Earnings vs. Forecast

Datadog’s Q2 EPS of $0.46 outperformed the forecasted $0.41, a 12.2% positive surprise. Revenue also surpassed expectations, reaching $827 million against a forecast of $790.9 million, a 4.54% surprise. This marks a significant beat compared to previous quarters, reinforcing Datadog’s strong market position.

Market Reaction

Following the earnings release, Datadog’s stock price surged 8.06% in pre-market trading to $148. This increase reflects strong investor confidence in the company’s performance and future prospects. The stock’s movement contrasts with its 52-week range, which saw a low of $81.63 and a high of $170.08, indicating renewed positive momentum. With a market capitalization of $46.07 billion and a beta of 1.02, Datadog shows strong market presence. For detailed valuation analysis and comprehensive insights, investors can access Datadog’s Pro Research Report, part of InvestingPro’s coverage of over 1,400 US stocks.

Outlook & Guidance

For Q3 2025, Datadog projects revenue between $847 million and $851 million, representing a 23% year-over-year growth. The company maintains its full-year 2025 revenue guidance at $3.312 to $3.322 billion, anticipating continued expansion in AI observability and platform capabilities.

Executive Commentary

CEO Olivier Pommel expressed optimism, stating, "We are extremely excited about our progress so far, against what we expect to be a generational growth opportunity." He emphasized the role of AI as a growth driver, noting, "AI is a tailwind for Datadog, as increased cloud consumption drives more usage of our platform."

Risks and Challenges

• Potential volatility in AI native customer usage.
• Competitive pressures from other cloud and AI service providers.
• Economic uncertainties that could impact enterprise spending.
• Rapid technological changes requiring continuous innovation.
• Dependence on large customers for a significant portion of revenue.

Q&A

During the earnings call, analysts inquired about the potential volatility in AI native customer usage and its impact on revenue. Executives highlighted strong product engagement and high customer retention, underscoring ongoing investments in AI and observability technologies to mitigate these risks.

Full transcript - Datadog Inc (DDOG) Q2 2025:

Conference Operator: Good day and thank you for standing by. Welcome to the Q2 twenty twenty five Datadog Earnings Conference Call. At this time, all participants are in a listen only mode. After the speakers’ presentation, there will be a question and answer session. To ask a question during the session, you will need to press 11 on your telephone.

You will then hear an automated message advising your hand is raised. To withdraw your question, please press 11 again. Please be advised that today’s conference is being recorded. I would now like to hand the conference over to your speaker today, Yuka Broderick, SVP of Investor Relations. Please go ahead.

Yuka Broderick, SVP of Investor Relations, Datadog: Thank you, Didi. Good morning, and thank you for joining us to review Datadog’s second quarter twenty twenty five financial results, which we announced in our press release issued this morning. Joining me on the call today are Olivier Pommel, Datadog’s Co Founder and CEO and David Obesler, 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 03/31/2025. Additional information will be made available in our upcoming Form 10 Q for the fiscal quarter ended 06/30/2025, 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.

Olivier Pommel, Co-Founder and CEO, Datadog: 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 users 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 are 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 $827,000,000 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 $165,000,000 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 add up more products, including our security offerings.

As a reminder, our security customers can identify and manage vulnerabilities with code security, cloud security, and 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 $100,000,000 in ARR, and is growing mid forties percent year over year. While we are pleased to achieve this milestone, we are still just getting started in solving customer problems in this area with new innovations such as our BIT AI security analyst. Moving on to R and 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 AI SRE agent to investigate alerts in coordinate incident response, Beats AI DAVE agent, an AI powered coding assistant to proactively fix production issues, and Beats AI security analyst to triage a lot of cloud SIEM signals. To further accelerate our users’ incident response, we announced 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 under greater incident. Second, we delivered a series of products to help customers ship better software with confidence.

With a Datadog internal developer portal, developers can ship better and faster by gaining a real time view into their software systems and APIs with a software catalog, by provisioning infrastructure, scaffolding new services, and managing code changes and deployments with self-service actions, and by following engineering and 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 CluedCodes from Anthropic. We worked 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 ID. Third, we are reimagining observability to meet our customers’ increasingly complex needs.

Our APM Latency Investigator formulates and explores hypothesis 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 FlexFrozen tier, customers can keep logs in fully managed storage for up to seven years and be able to search without data movement or rehydration. Archive search now enables teams to query archive logs directly in cloud storage, like Amazon S3 buckets or in the FlexFrozen 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 in 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 have prevent prompt injection attacks and data poisoning in runtime. And finally, we showcase 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 AI Agents 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 agent. And last but not least, and accelerated by our recent acquisitions of MetaPlan, Datadog now offers a complete approach to data observability across the entire data lifecycle, from ingestion 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 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 on silos across TINI. Now let’s move on to sales and marketing.

We had a number of great new logo wins and customer extensions this quarter. So let’s go through a few of those. First, we signed a 7 figure annualized expansion in a three year contract worth more than $60,000,000 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, they continue to migrate more applications to the cloud.

This customer is expanding to 21 Datadog products, with thousands of users who log in to the Datadog platform every month. Next, we signed a 7 figure expansion to an eight figure annualized contract with a leading US 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 $9,000,000 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 would adopt 19 Datadog products, and will consolidate a couple 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 in extra cost and lost engineering time. They are expanding to 21 Datadog products, including all of our security products, and replacing their paging solution with Datadog OnCall and incident management. Next, we landed a 7 figure annualized deal with a leading Brazilian e commerce company. These customers 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 to its cloud tech stack and so significant improvements in application stability and incident resolution times. This customer will start with seven data log products, including text 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 seven products to start, this customer will consolidate half a dozen tools while meeting their PCI compliance requirements.

Finally, we welcome back a leading US 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, alert fatigue, and poor customer experience. In returning to Datadog, they plan to add up six products, including replacing their paging system with Datadog Onco. And that’s it for another productive quarter of our go to market teams, who are now very hard at work on a BDQ3. Before I turn it over to David for a financial review, I want to say a few words on our longer term outlook.

There’s 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 a million dollar a year with us, and more than 80 who are spending more than a $100,000. And they include eight of the 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, element observability, AI agent observability, and data. 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 road breaking research. A great example of that is our TOTO financial 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.

David Obesler, CFO, Datadog: Q2 revenue was $827,000,000 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 eight percent 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 six points last quarter and about two 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 in 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 Ali, 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

Olivier Pommel, Co-Founder and CEO, Datadog: production. Begin

David Obesler, CFO, Datadog: Now regarding usage growth by customer segment, in Q2, 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 twelve month trailing net retention percentage was about 120, higher than the high 110s last quarter. And our trailing twelve month gross revenue retention percentage remains in the mid to high 90s.

Now moving on to our financial results, first billings were $852,000,000 up 20% year over year. And remaining performance obligations or RPO was $2,430,000,000 up 35% year over year. Our current RPI 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 $669,000,000 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 $164,000,000 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 $13,000,000 We also experienced a rising impact from the weaker dollar and absorbed $6,000,000 of negative FX impact during Q2. Excluding those expenses, operating income would have been 22 percent in Q2 or 200 basis points higher. And now turning to the balance sheet and cash flow statements. We ended the quarter with $3,900,000,000 in cash, cash equivalents and marketable securities. And our cash flow from operations was $200,000,000 in the quarter After taking into consideration capital expenditures and capitalized software, free cash flow was $165,000,000 for a free cash flow margin of 20%.

And now for our outlook for the third quarter and the remainder of fiscal twenty twenty five. 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 $847,000,000 to $851,000,000 which represents a 23 year over year growth. Non GAAP operating income is expected to be in the range of $176 to $180,000,000 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 $364,000,000 weighted average diluted shares outstanding. For fiscal twenty twenty five, we expect revenue to be in the range of $3,312,000,000 to $3,322,000,000 which represents a 23% to 24% year over year growth. Non operating GAAP operating income is expected to be in the range of $684,000,000 to $694,000,000 which implies an operating margin of 21%, and non GAAP net income per share is expected to be in the range of $1.8 to $1.83 per share based on approximately $364,000,000 average diluted shares. Some additional notes on our guidance, we expect net interest and other income for fiscal twenty twenty five to be approximately $150,000,000 And due to the impact of the recent federal tax legislation, we now expect cash taxes for 2025 to be about $10,000,000 to $20,000,000 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 twenty twenty five.

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 and A.

Conference Operator: Thank And our first question comes from Raimo Lenschow of Barclays. Your line is open.

Raimo Lenschow, Analyst, Barclays: Perfect. Thank you. Two quick questions from me. Olivier, like you talked about the AI contribution and to slowly broadening out. How should we think about it in terms of when this goes much broader into inference, etcetera?

So does that everyone like Barclays, JPMorgan, etcetera, they all kind of need to do more around observ observability because they’re going to do more inference, etcetera. So in a way, like OpenAI, etcetera, is just setting the scene for 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 on their productivity curve?

Thank you.

Olivier Pommel, Co-Founder and CEO, Datadog: Yeah. 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, know, or 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, you know, so you have, normal machine CPUs, some GPUs, quite a few, other components, you know, 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 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.

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 non deterministic anymore. They either are run by a model that is non deterministic by nature, or they’re running code that was not as carefully written as it used to be. It’s not completely written by humans, it’s 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 all. Brand new array 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 points 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.

Mike Cikos, Analyst, Needham: Thank you.

David Obesler, CFO, Datadog: Yeah, 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, the last part of twenty twenty five. 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 in productivity and performance, we see some good signs that that quota capacity is becoming productive.

Andrew DeGasperi, Analyst, BNP Paribas: Thank you.

Conference Operator: Our next question comes from Sanjit Singh of Morgan Stanley. Your line is open.

Sanjit Singh, Analyst, Morgan Stanley: Thank you for taking the questions and congrats on really stellar results this quarter. David, when I look at the guide, I mean, this is probably one of the more impressive guides coming out of a Q2 that I’ve seen in a couple of years. If I square that against the commentary that you guys made on the AI native cohort that look, there could be volatility from this cohort. When I kind of put those two together, the guidance is really strong. And so when I think about that potential risk, is it fair to assume that it’s not something that you’re seeing right now and may come to play later on down the road?

Because the guidance seems really strong. It doesn’t seem to at least on the face doesn’t seem to anticipate that much volatility from AI native cohorts.

David Obesler, CFO, Datadog: Yeah, I think we gave metrics indicating that based on what we saw in the quarter and we’re seeing now that the AI cohort continues to grow quite rapidly and we’re winning market share in that. And so, we incorporate that into the guidance is, as we discussed previously, we know that there might be volatility in usage or in as we negotiate contracts in unit rates. And so therefore, we adopt conservative assumptions as to that performance in the remainder of the year. It’s not something as you can tell from the growth metrics that we see yet in our results. But as we learned in the previous cycle of cloud natives, there can be volatility and we want to make sure we incorporate that in our guidance.

Sanjit Singh, Analyst, Morgan Stanley: Perfect. Then Olivier, with the new security disclosures, congrats on crossing the $100,000,000 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?

Olivier Pommel, Co-Founder and CEO, Datadog: Yeah. So we have a very good product set, and we mentioned products in there. There are a couple of those products that are really, I would say, reaching an inflection point in terms of what they’re doing on the customer side. When I think of where we’re successful today in security, we’re very successful at getting broad adoption, like a large number of customers, a few customers that are spending a million plus on security with us. So we’re good with the, we’re happy with the proof points we have there.

What we haven’t done very well yet is getting standardized adoption wall to wall in large enterprises. And that’s the next focus for us on the security side. And some of that is product work, but a lot of it is a few customizations to go to market there so we get better at selling enterprise wide security top down, which

Patrick Colville, Analyst, Scotiabank: is not something we have done a lot of

Olivier Pommel, Co-Founder and CEO, Datadog: in the past. So that’s sort of where we are as a product. So happy with where we are. A lot of groundwork has been done on the product side, but it’s quite a bit more work to be done and a ton more opportunity in front of us. That’s why we’re focusing on it.

Sanjit Singh, Analyst, Morgan Stanley: Appreciate the thoughts, Olivier. Thank you.

Conference Operator: Thank you. And our next question comes from Rangan of Goldman Sachs. Your line is open.

Koji Ikeda, Analyst, Bank of America: Hey, thanks for taking my question. 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? Really how that informs your second half guide? Thanks a lot. That’s it for me. Yes,

David Obesler, CFO, Datadog: 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, picked out 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.

Conference Operator: Thank you. And our next question comes from Mark Murphy of JPMorgan. Your line is open.

Yuka Broderick, SVP of Investor Relations, Datadog0: Thank you. My 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 is applicable and opening it up very broadly. The size of the data set 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 I have a quick follow-up for David.

Olivier Pommel, Co-Founder and CEO, Datadog: Look, we think there’s so much opportunity in automation with autonomous AI agents. Like we really broke it out in three different categories so far. And one is the SRE and responding to alerts and investigating and maybe auto remediating those issues. Second one is coding, fixing issues that we find in the code that happened in production and verifying the fixes ourselves. And the last one is security on investing in security signals on our own so that customers don’t have to do that themselves.

There’s so much that needs and that can happen there. A lot of it is going to depend on great research, which is why we built a research team and which is why we developed and released with open weight research models already. Of course, the next step after using this research model is to incorporate them into the product. So that’s also one I think working on right now. But there’s just so much of opportunity in front of us there that we’re at this point we’re happy we got a great start.

We got fantastic results in our first release as a research output is really like a state of the art model that beats every single other model in a category that has seen quite a bit of action over the years. Time series forecasting has very wide applicability in a lot of different domains. So I think it shows that we can perform at the highest level there and I think it’s a great sign of things to come in terms of AI automation and AI agent.

Yuka Broderick, SVP of Investor Relations, Datadog0: Okay, thank you. And then David, we keep pointing out that Datadog is one of the only software company that’s investing seriously in headcount growth and it feels like that is paying top line dividends pretty tremendously today. We noticed the R and D spending is up noticeably in Q2. Just wondering what are the mechanics that are driving that on the R and 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?

David Obesler, CFO, Datadog: Yeah, in R and D, as we talked about, we had an aggressive investment plan and we’ve been able to execute and think our recruitment, credit to our recruitment team, we’ve been able to get people in the door, the right people earlier in the year. There are some things within that around FX that weigh a little bit on it, because as you know, we do have a significant R and D center in Paris, but I think the overall trend is the execution and recruiting. We talked about some of the factors in Q2 that caused the operating income to increase greater, to increase at a rate of 36%. And some of those are things like the timing of DASH, we talked about 13,000,000 the FX and I think that we have good line of sight on the drivers in R and D, both in terms of, as we talked about, and some of the operating expenses have some seasonality in it. The one thing I would add, which is that we also are spending more on AI training and inference in R

Olivier Pommel, Co-Founder and CEO, Datadog: and D. If you compare it to two past years, and output of that is the 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 and D investment to change in the future, although I think we expect the same envelope to be what we use moving forward.

David Obesler, CFO, Datadog: Yeah, I’ll add that and really call out to our R and D team and our FinOps that we said last quarter that we were going to focus on how we use cloud. That applies to both the gross margin and as you know, we dog food, we use a lot of our applications internally. And we were in Q2. In that run rate, we expect to continue forward in optimizing our cloud usage, which will have an effect on the margins and the OpEx growth rates as we proceed through the year.

Yuka Broderick, SVP of Investor Relations, Datadog0: Thank you very much.

Conference Operator: Thank you. And our next question comes from Koji Ikeda of Bank of America. Your line is open.

Koji Ikeda, Analyst, Bank of America: Yes. Hey, guys. Thanks so much for taking the questions. 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. So I know there is some concern about there. David, you mentioned potential volatility.

So I really wanna ask about how you’re feeling about contract visibility. Thanks.

Olivier Pommel, Co-Founder and CEO, Datadog: Mean, look, we can’t really speak about any specific customers. As a reminder, any individual customer can do whatever they want. They’re the heroes of their own stories and we can’t really speak for them. I would say we have strong product engagement from our top customers in general. We are working on making on making Datalog the very best platform for every company at any scale, including scale that has never been seen before in companies with high growth.

And I would say that’s about it. When you look at the way we forecast the business, remember that we are overall extremely high retention product. For most customers it’s not rational to do it themselves, build their own solutions. We have many customers who did turn to build themselves who come back afterwards and we named one on the call today. So we feel confident about the way we forecast the business and the mid to long term there.

Of course, as we renegotiate with customers, as they increase volume, etcetera, etcetera, what typically happens is we see short term drops and long term growth in the revenue that’s associated with them. And that’s the way we’ve always deployed it.

Koji Ikeda, Analyst, Bank of America: Thanks so much. And I did have a follow-up on security. And so, you know, it sounds, I mean, great to hear about the milestones, dollars 100,000,000, around 40%. And so thinking about the product set, how are you thinking about expanding the capabilities from here? Are you focused on more organic, inorganic?

And maybe an update to your M and A philosophy. I I guess the question here is, you willing to go much bigger to supplement your security strategy? Thank you.

Olivier Pommel, Co-Founder and CEO, Datadog: Look, we’re looking at a number of different things in security that there’s a lot of companies out there. There’s a lot of product areas we cover already and a lot of more product areas we can cover. It’s also a space where you need to cover a lot of the, we call them boring must have table features on one end, but also there’s quite a bit of investment in the future with the way the whole field is being disrupted with AI. So there’s quite a bit of work to be done there. You should expect us to do more M and A around that as we do in the rest of the business as there is a lot of assets out there and there’s a lot of opportunities to grow.

Thank you so much.

Conference Operator: Thank you. And our next question comes from Karl Keirstead of UBS. Your line is open.

Yuka Broderick, SVP of Investor Relations, Datadog1: Okay, great. Thanks. 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?

Thank you.

David Obesler, CFO, Datadog: Yes, I would say like we talked about last quarter, this isn’t about the AI and margins, the AI cohort versus non 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.

Olivier Pommel, Co-Founder and CEO, Datadog: Yep. And I will double up on the this was a bit of an infomercial. We so we did we did see, you know, as we mentioned last quarter, we were seeing gross margins going down a little bit further 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 products and our profiling product largely. And then we, in a matter of months, we really turned up like 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 right now to bring to all of our customers, so they can get the same effect and they can see their margins go up as well.

Yuka Broderick, SVP of Investor Relations, Datadog1: 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 there’s some other drivers you have in mind?

David Obesler, CFO, Datadog: 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.

Yuka Broderick, SVP of Investor Relations, Datadog0: Got it. Thank you both.

Conference Operator: Thank you. And our next question comes from Mike Cikos of Needham. Your line is open.

Mike Cikos, Analyst, Needham: Hey, guys. Thanks for taking the question here. I just wanted to double back on the enterprise segment and just Mhmm. This is for Ali. But if I’m thinking about it, I know that we have the enterprise demonstrating this stable growth.

Is it fair to assume like, is the analogy for enterprises who are more traditionally using CPU versus the AI native companies or growing investment in GPUs, is it analogous to, like, fifteen years ago where we saw, hey, on prem continues to see investment, but maybe more dollars are going towards cloud. Is is that, like, a fair analogy when we think about what sort of behavior is exhibited by these different customers and where Datadog is headed?

Olivier Pommel, Co-Founder and CEO, Datadog: I don’t know if you can say it exactly this way because at the time the on prem versus cloud is tended to be different customers. Whereas today, sorry, this would be the customers. Whereas today, like the AI natives and enterprise are different companies altogether. I think the main difference is the AI natives have businesses that are growing very fast and infrastructure that are growing very, very fast in sales. Whereas the enterprises are still going through a controlled migration from on prem into the cloud.

And the rate there is more limited by their bandwidth to undergo that migration as opposed to being driven by an explosion of traffic on the demand side for them. If I look at our enterprise segment in general, we see great trends in terms of the bookings, terms of new products attached, new customers, things that these customers are buying from us that are net new. But we see that the usage growth is a bit more moderate than that at this point. And I think that speaks to the bandwidth on there and just to move the workload and to go fast there. And that relates in part to the fact that a lot of our attention is spent on figuring out what AI technologies they’re going to adopt and how they’re going to ship these AI applications into production.

Overall, we see that rate as stable. So we think this is healthy. But we think we will see more growth from these enterprise customers as they actually get into production with the AI applications in the future.

Mike Cikos, Analyst, Needham: Understood. Thank you for that. And congrats on the security. I didn’t want to leave hanging. Don’t know if we got commentary on it, but if could we please get an update on FlexLogs?

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 FlexLog side of the house.

Olivier Pommel, Co-Founder and CEO, Datadog: Yeah, all of the big deals with Enterprise customers now involve FlexLogs in some form. And that’s a story that resonates very well when we, especially when we have customers that want to migrate from the legacy solutions from logs. So there’s a number of things that we’re working on with them, particular, making sure the migration is painless for them. There’s a number of things that we are investing in on that side, but FlexLog is a big draw for them as it really changes the picture economically and the predictability of the observability cost for them, which is a major concern for data intensive, data intensive plus observability such as logs.

Mike Cikos, Analyst, Needham: Great, thank you guys.

Conference Operator: Thank you. And our next question comes from Jacob Roper of William Blair. Your line is open.

Yuka Broderick, SVP of Investor Relations, Datadog2: Yeah, thanks for taking the questions. 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?

Olivier Pommel, Co-Founder and CEO, Datadog: Well, I mean, if I knew when it was going to happen, would tell you. The nature of our customers is that they grow, they have their own businesses to run, they have their own constraints. To have 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 the cadence for customers to figure out what they need to optimize and what they need to do for the future.

But we never know it’s whether it’s going to happen this quarter, next quarter, in three quarters, next year, never. That’s really hard to tell.

Yuka Broderick, SVP of Investor Relations, Datadog2: Okay. That’s helpful. And then could you also talk about the the uptake and feedback that you’re you’re getting for your own AI solutions like Bits AI, the new observability agents, and and when you think those could really start layering into the model?

Olivier Pommel, Co-Founder and CEO, Datadog: Yeah. So I know the initial response to to to the AI agents is really, positive. So the the AI SRE actually works surprisingly well. I If you think of how far technology has grown in a number of couple of years. And so right now we’re busy basically shipping it to as many customers as we can and enabling the customer with it.

And that’s a bigger real focus in the business as well. Like 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 sell all those customers. That’s the core focus of the business today. But the initial response is very positive. We’ve had customers purchase an extract Q4 pretty quickly in their trial.

And so we feel very good about it.

Yuka Broderick, SVP of Investor Relations, Datadog2: Very helpful. Thanks for taking the questions and congrats on the great results.

Conference Operator: Thank you. And our next question comes from Brent Thill of Jefferies. Your line is open.

Yuka Broderick, SVP of Investor Relations, Datadog3: Good morning, David. Just on the quota carrying rev capacity, and know you’ve been investing aggressively ahead of the curve. 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 rep, quota rep count looks like through the rest of the year.

And if you can shape the year, how that looks versus ’24?

David Obesler, CFO, Datadog: Yeah, what we’re doing is we’re executing the plan we entered the year with. We knew, I think we said that we had under invested in go to market and looked at that with the white space, etcetera. And I would say we’re successfully executing that. The plan was a little more front weighted given our appetite for taking advantage of that opportunity, but we’re executing that. And we will look towards the end of the year as we plan for next year on the metrics around that and try to calibrate how we look at that growth next year.

Yuka Broderick, SVP of Investor Relations, Datadog3: Okay. And Olivia, I’m just curious, many CEOs are either holding headcount flat or down. We’ve seen Meta headcount down from two years ago, Microsoft headcount flat. Others, talent here saying they’re going to shrink headcount in 10x revenue. Do you believe you can become more efficient with fewer or do you think that that model doesn’t apply that you’re seeing at other software companies?

Olivier Pommel, Co-Founder and CEO, Datadog: I mean, look, definitely the spend is shifting a little bit on the engineering side. As I said, we compute just see more AI training, AI inference. And so that’s definitely changing a bit of the balance between what you have humans do and what you offer to GPUs. That being said, we’re still completely constrained by the amount of product we can put up there. There’s a ton of opportunity in every single direction we look, whether that’s on the automation, whether it’s on the security side, whether that’s in the new areas such as data observability or experimentation that we’re going after.

And so for us, they’ve grew this very strong ROI in the ads that we’re making at the moment.

Yuka Broderick, SVP of Investor Relations, Datadog0: Great, thanks.

Conference Operator: Thank you. And our next question comes from Andrew DeGasperi of BNP Paribas. Your line is open.

Andrew DeGasperi, Analyst, BNP Paribas: Thanks for taking my question. 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?

David Obesler, CFO, Datadog: 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 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.

Andrew DeGasperi, Analyst, BNP Paribas: Thanks. 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 the attach rates in terms of the three or four more products per customer accelerate at this level? I know they’ve been ticking up about a point every quarter. Just wondering if that’s something we should be seeing.

David Obesler, CFO, Datadog: Well, I think broadly, we expect the trends that we’ve seen of landing with some of the core products in the pillars and then expanding to continue, as the platform has expanded, we’ve tended to land with more products, but those trends that we evidenced in the script are we expect to continue in the geographies.

Olivier Pommel, Co-Founder and CEO, Datadog: And keep in mind, a lot of the so when you’re in the field, it’s always easy to upsell a customer than to land a new customer. And a lot of the work we do in territory management and comp planning for the sales team is really to make sure that there’s enough of an incentive to go and look for new customers. So we keep driving number of new customers out as well. So there’s this balance always between do you direct the Salesforce at upsetting existing customers or or any new customers. Thank you.

Conference Operator: Thank you. And our next question comes from Patrick Colville of Scotiabank. Your line is open.

Patrick Colville, Analyst, Scotiabank: All right. Thank you for squeezing me in. And I guess I just wanted to say before I ask my question, congrats on the and 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 kind of 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.

Olivier Pommel, Co-Founder and CEO, Datadog: Yeah, I mean, there’s always been competition in the field. Now, as I like to say, when I first fundraised for DARE DUG, the world that was coming back to me every single time with every single know I was getting from all EDCs was crowded space. And so throughout the last of the company, there’s been not only incumbents that we’ve mostly have been in the market now, but also a steady stream of new entrants that we also have in a year after year have in the market. There’s always new companies, always folks that are building new things in observability. I think it’s very attractive for engineers to build that.

I would know something about it. Generally speaking, the community landscape hasn’t changed much in the past ten to fifteen years, about the same. The way we win and we will keep winning is by offering an integrated platform that solves as many problems as possible for our customers end to end. So we don’t just focus on one tool of the puzzle, we don’t just focus on one data store, one specific bridge that our customers might want to use. We solve the whole problem for them end to end.

And then in the long run, we win by being more innovative, by having an economic model that lets us invest more in R and D, develop more products, build our existing products into the future faster than anybody else can do, and cover more adjacencies faster than anybody else can do so they can have the broadest platform. So that’s the reason we win. And if you look at all of the companies you mentioned, none of them are in a position to do the same. And so that’s where we’re going to end up in the end. And I think that’s the end of the call.

That would be the last question. And just to close out, I want to thank our customers for working with us to bring all of those great new products to market. So we had a lot on our plate this year. You’ve seen that at Dash. Was amazing by the way to see all these customers and meet with them at Dash and see the reception we would get for new products.

And so I want to thank them, and I know we’re working with many of them on how these products are going to be adopted and what’s going to happen in Q3 and Q4. So again, thank you, and I will see you next quarter.

Conference Operator: This concludes today’s conference call. Thank you for participating, and you may now disconnect.

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