Sentry
Sentry connects errors, logs, replays, spans, profiles, and metrics to help developers monitor applications and debug production issues.
Identification marks
- Price
- Price not listed
Switch memo
- Leave your current tool if
- Leave your current tool if your errors, logs, session replays, traces, profiles and metrics live in separate places and you want them connected by one trace, with each incident mapped automatically to the release, pull request and owner.
- Migration effort
- Sentry says setup takes about five lines of code: install the language SDK, no host agents. Next.js, Angular, Flutter and React Native have one-command wizards. Each service still needs the SDK added and initialized, and trace sampling must be adjusted for production.
- You give up
- You give up agent-free, host-level infrastructure monitoring; Sentry instruments code, so nothing is captured until the SDK is in each application. Seer's root-cause analysis depends on Sentry already holding your logs, commits and traces.
- You gain
- You get error tracking tied to performance data, replays and logs, Seer for root-cause explanations and merge-ready patches, agent tracing for tool calls, output problems and spend, and integrations with GitHub, Slack, Jira, Linear and coding agents via an MCP server.
The cost check
Sentry is application monitoring software built around error tracking, with performance monitoring, logs, session replays, tracing, profiling and metrics tied together by the same trace. It adds an AI debugging agent called Seer and agent tracing for AI workloads, and it installs through language SDKs rather than host agents.
What you gain
- SDK-based setup that Sentry says takes about five lines of code, with one-command wizards for several frameworks
- Errors, logs, replays, spans, profiles and metrics connected by the same trace
- Seer AI debugger that explains root causes and proposes merge-ready patches
- Automatic mapping of incidents to release, PR and owner
- Integrations with GitHub, Slack, Jira, Linear and coding agents via an MCP server
What you give up
- Every service needs the SDK added and initialized in code, which is migration work across a large codebase
- Trace sampling must be tuned; the Python example captures 100% of transactions and Sentry recommends adjusting this in production
- Seer's usefulness depends on Sentry already holding your logs, commits and traces
Teams that switch
- Development teams that want error tracking and performance data linked by one trace
- Teams shipping web, mobile and backend code across many languages and frameworks
- Teams building AI agents that need visibility into tool calls, output problems and spend
- Teams that want issues tied automatically to releases, PRs and code owners
Stay put if
- Teams that want monitoring without adding SDKs or initialization code to their applications
- Teams looking for host-level, agent-based infrastructure monitoring rather than code-level instrumentation
The longer story
Sentry presents itself as a developer-first monitoring tool. Its core is error tracking, and it extends that with performance monitoring, log monitoring, session replays, distributed tracing, code profiling and metrics. The pitch is that errors, logs, replays, spans, profiles and metrics are all connected by the same trace, so a team can move from an issue to its context to a fix. Typical uses named on the site include debugging 500 errors, tracing slow requests, replaying failed fetch() calls, and catching slow queries, N+1 queries and request timeouts.
For teams weighing a switch, setup effort is the main draw. Sentry says monitoring takes about five lines of code: drop in the SDK, with no agents to install. Setup wizards exist for frameworks such as Next.js, Angular, Flutter and React Native, and there are SDKs for Python, Node.js, React, .NET, Go, Swift, Ruby, PHP, Laravel, Spring Boot, Vue, Svelte, Astro and others. Instrumentation still lives in your code, though, so each service needs its own SDK and initialization, and settings like trace sampling need tuning for production.
Sentry also covers newer workloads. Seer, its AI debugger, uses logs, commits, traces and stack traces to explain why code failed and to generate merge-ready patches. Agent tracing tracks output problems, bad tool calls, and the spend and performance of AI agents. Incidents are mapped automatically to the release, pull request and owner, and integrations reach GitHub, Slack, Jira, Linear and coding agents through Sentry's MCP server.
Before you switch
How much work is it to get Sentry running?
Sentry says it takes about five lines of code. You install the SDK for your language, and some frameworks such as Next.js, Angular, Flutter and React Native have a one-command setup wizard. No separate agents need to be installed.
Which languages and frameworks does Sentry support?
Supported platforms include Next.js, Angular, Android, iOS, Flutter, React Native, .NET MAUI, Python, Node.js, React, .NET, Go, Swift, Ruby, PHP, Laravel, ASP.NET Core, Spring Boot, Vue, Solid, Svelte, Astro and JavaScript.
What is Seer?
Seer is Sentry's AI debugging agent. It uses Sentry context such as logs, commits, traces and stack traces to explain why code failed and to generate merge-ready patches.
Can Sentry monitor AI agents?
Yes. Agent tracing helps catch unexpected output issues and bad tool calls, and shows spend and performance across your agents.
What does Sentry integrate with?
Sentry names GitHub, Slack, Jira and Linear, and it connects to coding agents through its MCP server.
Checked against
What it does
- Error tracking
- Performance monitoring
- Distributed tracing
- Log monitoring
- Session replay
- Profiling
- Metrics monitoring
- AI debugging
- Agent tracing