Sentry vs Datadog: Error Tracking or Full APM?
Sentry focuses on developer-centric error tracking and performance; Datadog is a broad full-stack observability platform spanning infra, APM, and logs.
Sentry excels at capturing exceptions with rich stack traces, release health, and frontend/backend error context, priced for development teams. Datadog spans the whole stack - infrastructure, APM, logs, RUM, security - making it an operations-wide platform at higher cost. They overlap on performance monitoring but differ in scope: Sentry favors deep error/release insight; Datadog favors broad infra-to-app observability.
| Sentry | Datadog | |
|---|---|---|
| Focus | Errors, releases, perf | Full-stack observability |
| Audience | Developers | Dev + ops |
| Breadth | App-centric | Infra, APM, logs, RUM |
| Cost | Lower, focused | Higher, broad |
| Best for | Error/release tracking | End-to-end ops visibility |
Use case and scope
Sentry suits teams that primarily want excellent error tracking, release health, and code-level performance context. Datadog suits teams needing infrastructure metrics, distributed tracing, logs, and security in one place. Many teams run both: Sentry for developer-facing errors and Datadog for operational observability.
In CI
Sentry pipelines upload source maps and create releases on deploy; Datadog ingests CI metrics and deployment markers. Either integrates from managed runners, where faster runners shorten build steps before release/upload.
The verdict
Primarily want deep error tracking, release health, and code-level performance: Sentry. Want broad infrastructure-to-application observability across metrics, traces, and logs: Datadog. They are complementary - Sentry for developer error context, Datadog for ops-wide visibility.