My thoughts on $DDOG Q1 2026

This was a very strong quarter, significantly above my expectations. Revenue growth accelerated to +32.2% YoY, and the company beat its own Q1 guidance by 4.7%, the largest beat in the past three years. If Datadog beats Q2 guidance by a similar margin, revenue growth could accelerate further to +36.8%. Full-year guidance was raised by 5.9%.

Billings growth accelerated to +37%, RPO growth reached +51%, and cRPO growth was approximately +45%, all outpacing revenue growth and indicating a continued acceleration trend.

Compared to Q1 last year, non-GAAP gross margin stabilized at 80.2%. Operating margin and net income margin also improved slightly.

Retention increased to approximately 123%, easing concerns about customer churn, including fears related to OpenAI transitioning away from Datadog toward ClickHouse.

Net new ARR reached $212.9M, growing +123% YoY, representing a record Q1 net new ARR addition in the company’s history.

SBC/revenue remains relatively high but declined to 20%.

Management sees AI agents as a major usage tailwind. AI training is becoming a real market for Datadog, with customers using Datadog to optimize hyperscale AI training workloads across large parallel GPU clusters.

LLM Observability and GPU Monitoring are promising growth areas for Datadog. The company launched GPU Monitoring to help customers understand GPU fleet utilization, workload efficiency, thermal and power characteristics, and interconnect performance.

What I liked most was the strong addition of $100k+ customers, with +240 new customers added, a record-level addition. Customers using products across every category increased, and customers using 10+ products rose from 9% last quarter to 11%. Platform adoption is clearly succeeding, with customers adopting more and more products. It’s also worth noting that $100k+ customers are no longer considered large customers for Datadog, as they now represent around 90% of the customer base. Management also highlighted several very large customer wins.

Datadog is used to monitor AI infrastructure, while Datadog itself is also leveraging AI. Bits AI falls into the category of “AI for Datadog.” Datadog’s usage-based model can benefit from both human and agent usage, making the rise of Agentic AI a significant tailwind for the company.

Datadog is a platform company, benefiting from platform consolidation. Datadog provides unified visibility across infrastructure, applications, logs, user experience, security, LLM workloads, GPU infrastructure, and AI agents.

Management also noted that the AI race is forcing even major cloud providers to focus internal resources on their core competitive advantages while outsourcing tooling development. This is supported by the fact that Datadog landed AI research divisions at two of the world’s largest technology companies.