Datadog AI

Continuously scans metrics, APM, and logs to surface anomalies, error rate spikes, and dependency regressions without manually configured thresholds. Includes root cause analysis linking anomalies to upstream deploys or infrastructure changes.

Generative AI co-pilot for incident response that answers natural-language questions over telemetry, summarizes incidents, drafts postmortems, and suggests remediation steps based on historical patterns.

Purpose-built tracing for GenAI applications capturing prompts, completions, tokens, latency, and cost across major model providers, plus quality evaluation pipelines for monitoring model output regressions.

ML-driven monitors that project time-series metrics into the future for capacity planning and identify single-host or single-pod outliers within larger fleet baselines.

Automatically groups similar log lines into patterns so engineers can spot the dominant error signatures in millions of lines without writing regex queries manually.

Clusters duplicate exceptions across services using ML, so a deploy regression appears as one issue with all impacted versions and users rather than thousands of individual stack traces.