Datadog, Inc.

Good day and thank you for standing by. Welcome to the Q2 2025 Data Dog Earnings Conference Call. At this time, all participants are in a listen-only mode. After the speaker's presentation, there will be a question and answer session. To ask a question during the session, you will need to press star 1-1 on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, please press star 1-1 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.

Thank you, Dee Dee. Good morning and thank you for joining us to review Data Dog's second quarter 2025 financial results, which we announced in our press release issue this morning. Joining me on the call today are Olivier Plamel, Data Dog's co-founder and CEO, and David Obstler, Data Dog CFO. During this call, we will make forward-looking statements, including statements related to our future financial performance, our outlook for the third quarter in the fiscal year 2025, and related notes and assumptions, our growth 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 in our upcoming form 10-Q for the fiscal quarter ended this year in the fiscal year 2025 and other filings for the SEC. This information is also available on the Invest Relations section of our website along with a replay of this call. We will discuss non-GAP financial measures which are reconciled to their most directly comparable GAP financial release in the tables in our earnings release, which is available at .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 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 growth 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 Apple and Metaplan acquisitions. We ended Q2 with about $3,850 customers with an ARR of $100,000 or 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 add up more products, including our security offerings. As a reminder, our security customers can identify managed vulnerabilities with code security, cloud security, and sensitive data scanner. And they can detect and protect from attacks with app and API protection, welfare protection, and cloud team. We are pleased that our security suite of products now generates over $100 million in ARR and is growing mid-40s percent year over year. While we are pleased to achieve this milestone, we're still just getting started in solving customer problems in this area with new innovations such as our Beats 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 Beats AI SRE agent to investigate alerts and 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 scene 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 and their grid incidents. 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 subject catalog, by provisioning infrastructure, scaffolding new services, and managing code changes and deployments with self-service actions, and by following engineering and 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 Codecode from Atomic. 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 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 APM data to detect issues and propose fixes before they become problems. We announced a FlexFrozen tier so 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 archival logs directly in cloud storage like Amazon S3 buckets or in the FlexFrozen tier. And Datadog now supports advanced 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 points to prevent poisoning in runtime. And finally, we showcase our new -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 platform such as CoreWave and LambdaLabs. 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 agents. And last but not least, and accelerated by our recent acquisitions of Metaplane, Datadog now offers a complete approach to data observability across the entire data life cycle from intention 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 this validates our approach to deliver a unified platform which represents 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 seven figure annualized expansion in a three-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 data set 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 data platform every month. Next, we signed a seven figure expansion to an eight figure annualized contract with the leading U.S. insurance company. Datadog is supporting these customers' 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 dozen tools across multiple business units. Next, we signed a nearly seven figure annualized expansion with the leading American media company, Google. This customer has about 100 observability tools across more than 300 business units and this tool fragmentation has resulted in inefficiencies in 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 seven-figure annualized deal with the leading Brazilian e-commerce companies. 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 into its cloud-based app and saw significant improvements in application stability and incident resolution times. This customer will start with seven Datadog products including sex logs. Next, we landed a seven-figure annualized deal with the delivery app of a major American retailer. This customer found our run and our 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 welcomed back a leading US mortgage company in a nearly seven-figure annualized deal. This customer had moved to using a dozen open source disconnected tools which led to fragmented visibility, a little fatigue, and poor customer experience. In returning to Datadog, they plan to adopt six products including replacing their paging system with Datadog Onco. And that's it for another productive order for -to-market teams who are now very hard at work on a BBQ three. 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 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 native applications with us. There are hundreds of customers in this group. They include more than a dozen that are spending over a million dollars a year with us and more than 80 who are spending more than $100,000. And they include eight 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 out. 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 launch BITS AI, SRE agent, DAVE 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 financial model for time series forecasting, which shows -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 the market. 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?