
Monitor, secure, and analyze your entire stack in one place
Datadog is a cloud-scale monitoring and observability platform that unifies metrics, traces, logs, and security signals into a single SaaS platform. It provides real-time visibility across infrastructure, applications, and user experience for engineering and operations teams running cloud-native and hybrid environments.
Real-time visibility into servers, containers, VMs, and cloud services with auto-discovered dashboards and alerting.
AI-powered distributed tracing with code-level insights across microservices, correlated with logs and metrics.
Centralized ingestion, parsing, and analysis of logs with live tail, pattern detection, and trace correlation.
End-to-end browser and mobile session tracking correlated with backend traces and infrastructure metrics.
Continuous compliance posture management detecting misconfigurations and runtime threats across cloud accounts.
Simulated user transactions from global locations to proactively detect API and web failures.
Tracks traffic flows between services and availability zones to identify latency and packet loss issues.
Maintain real-time visibility across AWS, Azure, or GCP tracking host health, containers, and auto-scaling events.
Trace requests end-to-end across microservices to identify slow queries and correlate latency with deployments.
Continuously audit cloud configuration against CIS benchmarks and detect runtime threats.
Centralized log search with pattern detection and trace correlation to reduce incident resolution time.

High-performance cloud compute, GPU, and bare metal across 32 global data centers

Open-source LLMOps platform for prompt management, evaluation, and observability

AI-powered autonomous monitoring that detects revenue-impacting anomalies in real time

Scalable, free, and self-hosted PaaS — Heroku on steroids
Best full-stack observability for teams running distributed services — Datadog's unified platform provides the end-to-end visibility that growing architectures demand.
The observability layer for engineering organizations — connects runtime metrics, CI pipeline health, and security monitoring back to GitHub deployments and releases.
Monitors AI agent workflows, LLM latency, token costs, and model quality for production AI applications.
Out-of-the-box integrations with Kubernetes, Docker, Slack, PagerDuty, Terraform, and hundreds more.
Monitor AI agent decision paths, token costs, and model quality in production AI applications.
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