Directory Observability & MonitoringAPM & Distributed TracingSentry

Sentry

About Sentry

Sentry makes application monitoring software. It is installed as an SDK, with no agent to run. It links errors, logs, replays, spans, profiles, and metrics through traces. Seer, its AI debugger, uses Sentry data such as logs, commits, and traces to analyze and fix issues. It integrates with GitHub, Slack, Jira, and Linear, and with coding agents through Sentry's MCP server.

Products

Agent tracing: Monitors AI agent output, tool calls, spend, and performance.
Session Replay: Records user sessions, including clicks, navigation, and errors.

Used for

Debugging 500 errors, slow requests, and failed fetch() calls.
Finding slow queries, N+1 queries, and request timeouts.
Linking incidents to the release, pull request, and owner.
Checking pull requests against error and performance history before release.

Who it is for

Developers.

From Sentry's own website, read 2026-10-23. How we build listings.

How to evaluate APM & Distributed Tracing

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Sentry. It comes from our Observability & APM Platforms buyer guide.

22%

Telemetry Coverage & Correlation

Unified metrics, traces, logs, real-user monitoring, synthetics, and continuous profiling; automatic correlation across signals; service maps and topology; Kubernetes, container, serverless, and cloud-provider auto-discovery; breadth and quality of the integration catalog

22%

Cost Control, Cardinality & Data Governance

Pricing-unit transparency and overage terms; high-cardinality handling without runaway cost; ingest-time filtering, sampling, aggregation, and tiered retention; a native or compatible telemetry pipeline; per-team budgets, quotas, and chargeback visibility so spend is governable, not just observable

20%

Distributed Tracing & APM Depth

End-to-end trace correlation across services and async boundaries, tail-based and adaptive sampling, code-level and method-level profiling, error tracking and exception grouping, database and dependency visibility, and frictionless OpenTelemetry trace ingestion alongside any native agent

16%

AIOps, Anomaly Detection & Root Cause

Quality of automated anomaly detection and alert noise reduction, causal versus correlative root-cause analysis, forecasting and capacity signals, and emerging agentic remediation — judged on accuracy during a real incident, not on the marketing name of the AI engine

12%

OpenTelemetry & Openness

Depth of native OTel support (traces, metrics, logs) versus a proprietary agent; whether collection is genuinely vendor-neutral or quietly locks you in; query-language portability; export and data-egress freedom; and how much re-instrumentation a future migration would actually require

Scale, Reliability & Operability

Proven performance at your peak event and series volumes, multi-region availability and platform SLA, data-retention options, RBAC and SSO, and — if self-hosted — the operational burden of running and upgrading the backend at high cardinality

Sentry alternatives

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