ewsba.se
Strong Q1 financials: Datadog posted 32% year‑over‑year revenue growth to just over $1 billion, ARR topped $4 billion, and the company generated $289 million in free cash flow (29% margin) while holding $4.8 billion in cash and equivalents.
AI adoption and product momentum: Management said AI and non‑AI cohorts both accelerated, launching features like MCP Server, GPU Monitoring and Bits AI tools, with AI‑integrated customers (~20% of customers) contributing roughly 80% of ARR.
Guidance raised and record new‑logo wins: Datadog guided Q2 revenue to $1.07–$1.08 billion and FY26 revenue to $4.3–$4.34 billion, citing broad‑based ARR adds and record new‑logo bookings including large AI research deals.
Interested in Datadog, Inc.? Here are five stocks we like better.
The AI Fear Around Datadog Stock May Have Been Completely Wrong
Datadog (NASDAQ:DDOG) reported first-quarter 2026 results that management described as a “very strong start to 2026,” highlighted by accelerating revenue growth, record new-logo activity, and expanding product adoption tied to both AI and traditional observability demand.
Co-founder and CEO Olivier Pomel said Datadog delivered revenue growth of 32% year-over-year, accelerating from 29% in the prior quarter and 25% in the year-ago quarter. Revenue totaled $1.1 billion in the quarter and “exceeded $1 billion for the first time in Q1,” Pomel said, calling it “a big achievement.” CFO David Obstler later cited Q1 revenue of $1.01 billion, also up 32% year-over-year, and said 6% sequential growth was “the highest for a Q1 since 2022.”
→ Berkshire Hathaway’s Record Cash Hoard: Why and What's Next?
The Market Is Selling Everything, but These 5 Stocks Aren't Breaking Down
Pomel said Datadog ended the quarter with about 33,200 customers, up from about 30,500 a year ago. Customers with $100,000 or more in annual recurring revenue (ARR) rose to about 4,550 from about 3,770 a year earlier, and those customers represented about 90% of ARR, according to Pomel. He added that total ARR now exceeds $4 billion.
On profitability and cash flow, Obstler reported non-GAAP gross profit of $807 million and gross margin of 80.2%, down from 81.4% last quarter. Non-GAAP operating income was $223 million for a 22% margin, compared with 24% last quarter. Datadog generated $335 million in operating cash flow and $289 million in free cash flow, for a 29% free cash flow margin. The company ended the quarter with $4.8 billion in cash, equivalents, and marketable securities.
SaaS Apocoplyse Survivor? Why Datadog Could Be a Real AI Winner
Pomel said Datadog saw “broad-based acceleration of revenue growth across cohorts, including both our AI and non-AI customers.” He highlighted new land deals with “two of the world’s biggest AI research teams,” aimed at improving and optimizing training workflows, and said the company is seeing a “very real” move of AI applications into production across both AI-native and non-AI companies.
At the same time, Pomel emphasized faster growth from non-AI customers, saying non-AI customer revenue growth “accelerated again this quarter to mid-20s% year-over-year,” up from 23% last quarter and 19% in the year-ago period. Obstler echoed that the acceleration excluding AI-native customers was to the “mid-20s percent” and said results were broad-based across customer size, spending levels, and industries.
→ Insider Sales: Top AST SpaceMobile Insider Cuts Postion Over 30%
Obstler said AI-native customer growth continues to “significantly outpace” the rest of the business, and that cohort now includes 22 customers spending more than $1 million annually and five spending more than $10 million annually. He also said trailing-12-month net revenue retention was in the low 120% range, up from about 120% last quarter, while gross retention remained in the mid-to-high 90s.
Pomel framed the company’s AI strategy in two categories: “AI for Datadog” and “Datadog for AI.” On the “AI for Datadog” side, he said Datadog launched its MCP Server to general availability, allowing developers to access live production data to debug applications “directly in their AI coding agent or IDE.” He also highlighted Bits AI Security Analyst, which he said autonomously triages Cloud SIEM signals, investigates threats, and provides recommendations—reducing certain investigations “from hours to as little as 30 seconds.” Datadog also shipped Bits Assistant in preview for natural-language search and action across the platform.
On the “Datadog for AI” side, Pomel said the company launched GPU Monitoring, designed to help teams understand GPU utilization, workload efficiency, and other performance characteristics to improve GPU ROI and operational reliability. Pomel said Datadog now has more than 6,500 customers sending data for one or more AI integrations; while that represents about 20% of total customers, he said they account for about 80% of ARR.
Pomel cited several usage indicators he said reflect rising AI activity on the platform:
Bits AI SRE agent investigations more than doubled from December to March.
Spans sent to LLM Observability nearly tripled quarter-over-quarter.
MCP Server tool calls quadrupled quarter-over-quarter.
Bits Assistant messages increased by a factor of 12 over the same period.
Beyond AI, Pomel highlighted the general availability launch of Experiments, which he said works with feature flags and combines statistical methods with “real-time observability guardrails.” He also pointed to APM Recommendations, which analyzes telemetry across multiple Datadog products to identify performance and reliability issues and “explain how to fix them.”
Pomel said Datadog plans to open its next data center in the U.K., citing an opportunity to serve British customers as cloud adoption accelerates in regulated industries. He also said the company received FedRAMP High certification, allowing it to pursue U.S. federal agency workloads that require the designation. Obstler added that Datadog has been investing for years in public sector go-to-market capabilities, including channel partners, ahead of certifications.
Pomel described multiple customer wins during the quarter, including an eight-figure and a seven-figure annualized deal with AI research divisions at two large technology companies. He said these organizations adopted Datadog to reduce friction and optimize training workflows, including use of GPU Monitoring on large GPU grids.
Pomel also cited expansions and wins tied to product consolidation and newer offerings, including Flex Logs and LLM Observability. Obstler said new-logo annualized bookings set “a new all-time record by a significant margin” and were more than double the year-ago quarter, including wins that spanned observability as well as “newer products like security, Data Observability, and Flex Logs.”
For the second quarter, Obstler guided revenue to $1.07 billion to $1.08 billion, representing 29% to 31% year-over-year growth, with non-GAAP operating income of $225 million to $235 million (21% to 22% margin). He noted the DASH user conference will occur in Q2 and is expected to cost about $15 million, which is reflected in guidance. Non-GAAP EPS is expected to be $0.57 to $0.59 based on about 369 million diluted shares.
For full-year 2026, Datadog guided revenue to $4.3 billion to $4.34 billion (25% to 27% growth) and non-GAAP operating income of $940 million to $980 million (22% to 23% margin). The company guided non-GAAP EPS to $2.36 to $2.44 based on approximately 372 million diluted shares. Obstler also projected about $170 million in net interest and other income and cash taxes of $30 million to $40 million, while capital expenditures and capitalized software are expected to total 4% to 5% of revenue.
On the Q2 outlook, Obstler said the “bedrock” of near-term confidence is record ARR added in Q1, and that the ARR add was “very broad-based and was not very concentrated.” Pomel added that even excluding the customer that contributed the most revenue in Q1, Datadog still would have posted a record quarter for ARR adds, and noted the company also landed customers in Q1 that do not yet contribute revenue but are expected to be meaningful contributors in the future.
Datadog (NASDAQ: DDOG) is a cloud-based monitoring and observability platform that helps organizations monitor, troubleshoot and secure their applications and infrastructure at scale. Its software-as-a-service offering collects and analyzes metrics, traces and logs from servers, containers, cloud services and applications to provide real-time visibility into system performance and health. Datadog's platform is widely used by engineering, operations and security teams to reduce downtime, accelerate incident response and improve application reliability.
The company's product suite includes infrastructure monitoring, application performance monitoring (APM), log management, real user monitoring (RUM), synthetic monitoring and network performance monitoring, along with security-focused products such as security monitoring and cloud SIEM.
The article "Datadog Q1 Earnings Call Highlights" was originally published by MarketBeat.