Full Transcript: Datadog Q2 2026 Earnings Call

Datadog (NASDAQ: DDOG ) held its second-quarter earnings conference call on Thursday. Below is the complete transcript from the call. APIs provide real-time access to earnings call transcripts and financial data. Visit to learn more.

12 billion, up 36% YoY, surpassing their guidance. They ended with 33,400 customers, including 4,720 with ARR of $100,000 or more. The company launched over 100 new products and features, notably expanding Bits AI for DevOps and AI stack observability, and achieved significant customer wins including a $30 million TCV deal with a major online media company. 47 billion.

The guidance reflects a conservative stance due to a usage reduction from their largest customer. There is strong growth in AI-native customers and an acceleration in non-AI customer revenue growth to the high 20% YoY. Datadog's platform strategy is resonating with customers, leading to increased product adoption. Management emphasized the transformative impact of AI on business growth and highlighted ongoing investments in R&D and go-to-market strategies as key drivers for future growth.

Full Transcript OPERATOR Good day, and thank you for standing by. Welcome to the Q2 2026 Datadog 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 11 on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, please press star 11 again. Please be advised that today's conference is being recorded.

I would now like to hand the conference over to your first speaker today, Yuka Broderick, Senior Vice President of Investor Relations. Please go ahead. Yuka Broderick, Senior Vice President of Investor Relations Thank you, Lauren. Good morning, and thank you for joining us to review Datadog's second quarter 2026 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 2026 and related notes and assumptions, 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 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, 2026. Additional information will be made available on our upcoming Form 10-Q for the fiscal quarter ending June 30, 2026 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.

com. With that, I'd like to turn the call over to Olivier. Olivier Pomel, Co-Founder and CEO Thanks, Yuka, and thank you all for joining us. To go over Q2 results, let me begin with this quarter's business drivers.

Our revenue growth in Q2 has accelerated across our customer base. On one hand, our AI-native customer cohort continued to grow and diversify both in the number of customers we serve and the scale of those customers. But on the other hand, and as a great illustration of the breadth of trends across our business, revenue growth for our non-AI customers also accelerated again this quarter to the high 20% year over year, up from the mid-20s last quarter and 18% in the year-ago quarter. Overall, we continue to see healthy trends in customer demand.

Our broad base of customers, from the most nimble startups to the largest and most established enterprises, are all adopting AI. We think this is accelerating their usage of cloud and modern technologies, as well as their usage of the Datadog platform to observe, secure, and act on their cloud and AI workloads. 12 billion, an increase of 36% year over year and above the high end of our guidance range. We ended Q2 with about 33,400 customers, up from about 31,400 a year ago.

We also ended with about 4,720 customers with an ARR of $100,000 or more, up from about 3,850 a year ago. These customers generated about 91% of our ARR, and we generated free cash flow of $279 million with a free cash flow margin of 25%. Turning to product adoption, our platform strategy continues to resonate in the market. For example, 58% of our customers now use four or more products, up from 52% a year ago; 37% of our customers use six or more products, up from 29% a year ago; and 13% of our customers use 10 or more products, from 7% a year ago.

So we're landing more customers and delivering value across more products, and our products are broadly delivering strong growth in usage and ARR. As an example, RUM, or Real User Monitoring, now exceeds $200 million in ARR and accelerated at its scale to over 50% growth year over year. Our customers are sending more user sessions and using RUM in conjunction with our newer Product Analytics to optimize their business outcomes. Moving on to R&D, we held our Dash user conference in June where we announced over 100 exciting new products and features for our users.

So let's go through some of the announcements. First, we expanded Bits AI to accelerate and automate the DevOps loop. This is the loop that goes from detection to investigation to remediation that engineers go through each time something breaks. At Dash, we announced a lot of new Bits capabilities for the DevOps loop.

Bits can now create and maintain monitors, identify root causes within minutes of a negative signal, recommend and implement fixes, follow guardrails to add safety and controls, continuously learn and improve from prior incidents, and detect symptomatic behaviors early to repair infrastructure issues before they escalate. Second, we announced Bits AI products to address the development loop. This is the loop that goes from coding to delivery to evaluation that developers navigate to get code to production.

For this loop, Bits Release now acts as an AI release validation agent, analyzing the impact of code changes, running end-to-end checks, and verifying production rollouts. Bits Code generates code fixes, grounding every fix in reproduction behavior, and Bits Testing also automates synthetic test generation and madness. Third, we expanded Datadog for AI, or products that observe, secure, and optimize the AI stack from end to end. Data Observability enables companies to trust the data being used by AI with lineage quality monitoring and jobs monitoring.

Base Data Analysis uses a rich data context to accurately answer business questions, and Agent Console provides visibility into AI agent usage, cost, and effectiveness in agent observability. Our Patterns capability automatically clusters user interactions into behavior groups to identify quality or cost issues, and Bits Evalve handles the repetitive parts of the agent development loop in order to improve the outcomes of agents. Fourth, we are broadening our platform to ingest, correlate, analyze, and act on more data, whether on-prem or in the cloud.

In network monitoring, we launched Network Paths and Network Configuration Management to trace changes that cause complex network issues. Within database monitoring, Bits Database Optimizer now automatically simulates and evaluates the impact of AI-generated changes in order to optimize slow queries. In log management, Federating Logs enables users to query external data stores including Databricks and ClickHouse. And with Bring Your Own Cloud, or BYOC, customers can now use the full Datadog experience on logs that are kept within their infrastructure.

And we've also announced that we're bringing BYOC to metrics and traces as well. In the digital experience space, Journey Monitoring automatically gives a single shared view for every critical user flow. And for custom metrics data, we introduced infinite cardinality metrics, which allow our users to answer arbitrarily complex questions as they generate larger amounts of data with AI agents without incurring any extra costs. Finally, we launched a number of innovations to secure the AI stack and defend against a new class of AI-powered attacks.

AI Guard Agent Discovery finds and maps every known and unknown custom agent so security teams can see what is protected and what is not. AI Guard for Custom Agents provides runtime protections to block attacks that can only be detected with real-time observability data. AI Guard for Coding Agents applies the same deep observability to block malicious skills and packages in code, and we also announced runtime prioritization engine to cut vulnerability noise by over 95%. And finally, we expanded Bits Security Analyst to run on non-Datadog SIEMs so customers can benefit from the smarts and the learnings of our prod dataset regardless of which SIEM they deploy.

As we continue to innovate, we are being rightfully recognized by independent research. We are pleased to see that for the sixth year in a row Datadog has been named a leader in the 2026 Gartner Magic Quadrant for observability platforms. Let's move on to sales and marketing and look at a few of the deals our GTM teams have closed in what has been a very strong quarter. First, we landed a six-figure annualized deal with a Fortune 10 company.

This company is expanding its e-commerce business, and they plan to use Datadog Log Management alongside 10 other Datadog products to improve customer experience and business outcomes. This win validates our expanded go-to-market approach to focus on the world's largest companies and win opportunities in the most complex environments. Next, we landed seven-figure annualized deals with two NEO labs. These AI labs are rapidly scaling their AI model training workloads and preparing for major product launches.

By deploying observability using Datadog, they gain visibility across their training infrastructure and GPU fleets and can iterate faster on their AI models. They are also using Bits AI to rapidly build monitors, dashboards, and alerts for deep observability context. Next, we landed a seven-figure annualized deal with a South American bank. This bank's fragmented legacy monitoring stack and manual triaging caused significant application downtime that was often called in by customers.

By consolidating into Datadog with 11 products, this customer enables visibility from their mainframe all the way to their microservices and has already reduced mean time to resolution on live production incidents. They are adopting Cloud SIEM and Data Security and evaluating other Datadog security products to improve their security posture. Next, we signed a seven-figure annualized expansion for an eight-figure annualized deal with a Fortune 100 health insurance company. This customer's biggest standing point is to deliver great experience to their members throughout their care while protecting PII across dozens of business units.

Datadog's HIPAA compliance and PII handling in RUM, Log Management, and Cloud SIEM allowed us to differentiate and win over competitive solutions, and Bits AI Investigation is already speeding up incident resolution and reducing expensive escalations. This customer will expand to 19 Datadog products. Next, we signed a multi-year, over $30 million TCV deal with one of the world's largest online media companies. This customer chose to standardize on Datadog across its business, displacing four commercial and internal tools.

Datadog also proved value beyond core observability with Product Analytics, CI Visibility, Data Observability, and Cloud Cost Management. This deal includes our largest win to date for Bring Your Own Cloud, displacing their legacy commercial logging tool at petabyte scale. And finally, we signed a nine-figure renewal with a leading AI company. This long-time, very large customer uses 17 Datadog products to enable unified visibility on production workloads at a very large scale, albeit with a usage reduction starting in Q3, which we considered in our guidance and which David will speak to.

Before I turn it over to David for a financial review, let me offer a few words on our longer-term outlook. There is no change to our overall view that digital transformation and the cloud migration are long-term, secure growth drivers for our business. But we now have an additional growth driver with AI as we help our customers deliver value with this transformative new technology. We are tremendously excited about our opportunities in AI.

To summarize where we are and where we're going. First, AI is a tailwind for Datadog today as cloud consumption grows and drives more usage of our platform. As of Q2, over 750 AI customers used Datadog to monitor and improve their tech stacks. When we look at the largest companies driving AI, all 10 of the top 10 AI leaders are Datadog customers.

Beyond AI-native, we see AI activity growing across our broader customer base. We're also seeing signs of rapid growth in agentic activity, with the number of MCP tool calls quadrupling, quadruple again quarter over quarter, and growing more than 22x when compared to Q4 2025. Second, we are delivering AI for Datadog to deliver more value and greater platform capability to our customers. This includes our Bits AI products: chat, investigation, detection, code testing, release, and many, many others.

Third, next-gen AI introduces new complexity and observability challenges. We are addressing this with what we call Datadog for AI, to observe and secure the AI stack from end to end. This includes GPU Monitoring, Agent Observability, Agent Console, Data Observability, AI Guard, and many other products. Finally, our AI research team and our large volume of rich data using critical workflows enable us to conduct groundbreaking research.

We have shown some of our work already with the second version of our time series model, Toto, in May. Toto version two was exciting for two reasons. First, we've shown it to be state of the art on key benchmarks. But more importantly, we've demonstrated for the first time true scalability for time series models, allowing us to target the same improvement path language models have followed since 2020.

So now, beyond Toto, we are working on larger and more ambitious dedicated models, both training models to power Bits AI and bringing other modalities beyond time series data into world models that we think can lead to a step change in capabilities for our customers. And we plan to accelerate these research efforts with the acquisitions of Adaptive ML, which we closed in June. Because of all of that, now more than ever, we feel ideally positioned to have customers of every size and every industry as well as all types of users, whether humans or AI agents, so they can transform, innovate, and drive value through AI and cloud adoption.

And with that, I will turn it over to our CFO, David. David Obstler, Chief Financial Officer Thanks, Olivier. 12 billion, up 36% year over year. Within that, our 11% quarter-over-quarter revenue growth is the highest since Q2 2022, and our quarter-over-quarter revenue added of $115 million is a record by a significant margin.

We continue to see robust usage growth from existing customers as well as a strong ramp in our new customers. Revenue growth accelerated with our broad base of customers, excluding AI customers, to the high 20s year over year, up from the mid-20s last quarter and 18% in the year-ago quarter. We saw robust growth across our customer base, with broad-based strength across customer size, spending bands, and industries. Meanwhile, our AI customers continue to grow rapidly and diversify in the quarter.

This 750-strong customer group includes a broad range of AI startups as it has in the past, but now also includes hyperscalers using Datadog for in-house AI labs. In Q2, this includes 31 customers spending more than $1 million annually, of which 8 customers spent more than $10 million annually. We also achieved strong new logo dollar bookings, with particular strength in Enterprise where new logo annualized bookings more than doubled from a year ago, and we are seeing new logos ramping faster, contributing more to revenue growth. The portion of our year-over-year revenue growth that relates to new customers was about 30% in Q2, up from 25% in Q1.

Geographically, we're performing well in all regions, with growth acceleration across the regions. S. as well. In addition, we are executing strongly in LATAM.

Regarding retention metrics, our trailing twelve-month net revenue retention percentage was in the low 120s, similar to last quarter, and churn remains low with gross revenue retention in the mid to high 90s. We believe this metric highlights the mission-critical nature of our platform for our customers. Now moving on to our financial results. 18 billion, up 38% year over year.