Datadog vs Sentry

Pick Datadog if your team runs infrastructure monitoring at scale and you need an AI that can correlate metrics, traces, and logs across a dozen services in one query. Its 42 tools cover dashboards, alerting, and incident timelines—useful when you're debugging production outages or tuning autoscaling policies. Pick Sentry if your focus is application-level error tracking and you want the AI to triage exceptions, link stack traces to commits, and surface which release introduced a regression. Sentry's 50 tools lean into developer workflows: assigning issues, marking resolved, querying error frequency by environment. For engineering teams that already live in one platform, stick with it—both OAuth flows are straightforward. If you're choosing fresh, Sentry edges ahead for product teams shipping features weekly; Datadog wins for platform or SRE teams managing uptime. Switching later means re-wiring any automation that parses tool outputs (alert summaries, incident reports), typically a few days of work if you've built custom workflows around the MCP's data shape.