The AI-powered observability and security platform for cloud applications
HQ: New York, NY, United States | Founded: 2010 | Employees: ~10,000–11,000 (~10,690 official in recent datasets) | Stage: Public — NASDAQ: DDOG, IPO September 19, 2019 | Website: https://www.datadoghq.com
Datadog, Inc. is a New York-headquartered observability and security platform for cloud applications. Its agents and integrations collect metrics, traces, logs, events, real-user monitoring data, synthetic tests, security signals and increasingly LLM/GPU telemetry, then correlate them in one SaaS product used by engineering, security and product teams.
Olivier Pomel and Alexis Lê-Quôc co-founded Datadog in 2010 after building Wireless Generation, an education-tech company acquired by News Corp. They experienced firsthand the pain of stitching together Nagios, Graphite, proprietary APM tools and ticket systems, and built Datadog to be the correlated replacement. The company grew through the cloud-native boom of the 2010s, IPOd in 2019, and expanded from core infrastructure monitoring into APM, logs, RUM, synthetics, security cloud SIEM, database monitoring, CI visibility, product analytics, LLM observability and AI agents.
Today roughly 30,000+ organizations use Datadog (customer count varies by disclosure definition), including a large share of the Fortune 500. The platform strategy is multi-product attach: customers start with agents and metrics, then adopt logs, APM, security and AI ops. That attach engine is why revenue can still compound at 30%+ despite multi-billion scale, and why management frames the company as building the operating system for cloud and AI systems rather than a monitoring dashboard vendor.
Datadog's founding insight was that Dev and Ops were flying blind with different tools. By wiring agents, APM, logs, metrics, RUM and synthetics into one correlated platform, Pomel and Lê-Quôc created a product that engineering leaders could buy once instead of gluing together. That single-pane-of-glass thesis is why Datadog became the default observability vendor for cloud-native companies and why it now sits beside (and increasingly inside) security, CI/CD and AI operations.
The 2026 numbers re-accelerate after the 2023 optimization scare. Q1 2026 revenue grew 32% to $1.01B; Q2 2026 grew 36% to $1.12B with $279M free cash flow. About 4,720 customers now pay >$100k ARR (up 23% YoY) and 603 pay >$1M. Full-year 2026 guidance is $4.45–4.47B. Gartner named Datadog a Leader in the Observability Platforms Magic Quadrant for the sixth consecutive year. The product story has shifted from dashboards to agents: Bits AI SRE Agent, Bits AI Security Agent, Bits Code, Bits Chat, Bits Agent Builder, GPU Monitoring and an MCP Server all shipped to GA in the last year.
The strategic question is whether observability remains a distinct category or gets absorbed by AI agents that simply 'know' when systems break. Datadog's answer is to own both the data plane (telemetry from every layer of the stack) and the action plane (AI agents that diagnose and remediate). Competitors — New Relic, Grafana/LGTM, Splunk (Cisco), Elastic, Grafana, plus cloud-native tools — attack from every angle, and large customers still price-shop. But with $5B in cash, multi-product attach expanding and AI workloads creating net-new telemetry (LLM traces, GPU metrics), Datadog is better positioned than most 'old' SaaS names to be the operating system of the AI era.
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