--- title: 'Logfire vs Sentry: Complete Observability, Not Just Errors' description: Compare Logfire and Sentry. See why teams choose Logfire for complete observability—logs, traces, AI monitoring, and yes, errors too. canonical: https://pydantic.dev/logfire/vs-sentry last-reviewed: 2026-07-13 --- > Markdown version of [Logfire vs Sentry](https://pydantic.dev/logfire/vs-sentry) — the canonical HTML page. > > Site index: [/llms.txt](https://pydantic.dev/llms.txt) --- # Logfire vs Sentry Sentry catches exceptions. But when your AI agent fails, Sentry tells you something broke, not what the model was doing or why your agent produced the wrong answer without throwing an error. Logfire shows you everything: logs, traces, LLM calls, and yes, errors too — in one unified view. Complete observability for the AI era. [Try Logfire free](https://logfire.pydantic.dev/) ## Quick comparison | Feature | Logfire | Sentry | | -------------------- | --------------------------------------------------------------- | -------------------------------------------------------------------------------------- | | Primary Focus | Full-stack observability (AI, logs, traces) | Error monitoring + add-on AI observability | | AI/LLM Support | First-class, automatic LLM instrumentation | Basic (treats AI calls like any function; nothing captured unless exception is thrown) | | When you see AI data | Every request, full trace retained | Alerting optimized for error conditions | | Logging | Structured logs with full context | Limited, error-focused | | Live View | Real-time 'pending spans' | No equivalent | | Query Interface | SQL (PostgreSQL) | Custom UI | | Distributed Tracing | Full support for AI, databases, APIs, and services in one trace | Limited performance tracing | | Pricing | Per-span ($2/million) | Per-event + quotas | ## Why teams choose Logfire ### What Sentry misses when your agent fails Sentry only tells you something broke, but AI agents fail differently. Wrong answers, bad tool calls, and slow retrievals often occur but will not throw a single exception. Logfire captures every LLM call, tool execution, and database query in one trace, on every request, whether or not anything errored. You get full observability: structured logs with full context, distributed traces across your entire system, real-time monitoring with "pending spans," and automatic AI visibility. ### Built for AI from day one, not bolted on Sentry has expanded into AI observability, so you'll see prompts, token counts, and tool call errors when things break. Logfire was built for AI—one function call gives you complete LLM visibility whether or not something went wrong. Logfire's workflow is optimized for understanding what's happening continuously, in production. ### See it before it breaks, not after Sentry shows you errors after they happen. Logfire has "pending spans" to show you what's happening right now. Watch a slow agent run in real time, see which LLM call is taking too long, spot the tool execution that's hanging before the user times out. For AI applications where latency and quality are both production concerns, reactive error monitoring isn't enough. ### SQL-Powered Analytics across your entire stack Query your traces with SQL. "Show me all FastAPI requests that called our LLM more than 3 times." "What's the average token usage by endpoint?" AI assistants are excellent at writing these queries for you. Point your coding agent at your Logfire data via our MCP server to get answers to arbitrary questions about production behavior, something no proprietary query UI makes easy. ## Which should you choose? ### Choose Logfire if... - Your AI agent fails silently without throwing exceptions and you can't diagnose why - You want logs, traces, AND error tracking in one tool - You're building AI applications and need to observe prompts, responses, token usage - You want to see what's happening right now, not just errors - You want to query your data with familiar SQL - You don't want to juggle Sentry + logging service + APM tool ### Choose Sentry if... - Your primary need is catching and triaging errors - You need robust JavaScript error tracking with source maps - You rely on deep integrations with Jira, GitHub Issues, or Linear, etc. - You don't need full-stack trace context - You want battle-tested error handling with extensive documentation ## FAQ **Can Logfire replace Sentry?** For many teams building AI applications, yes. Logfire provides complete observability including error tracking, structured logs, distributed traces, and full AI visibility. Many teams do replace Sentry entirely. However, if you rely on Sentry's specific issue tracker integrations or deep JavaScript error tracking with source maps, you might run both tools or evaluate based on your specific needs. Sentry is for error alerting and triage; Logfire is for understanding what your AI agent was actually doing, for debugging and improving. **Does Logfire track errors?** Yes. Logfire captures exceptions and errors as part of its full observability. You get errors with complete context — the distributed trace showing what happened before the error, the logs from that session, and for AI applications, the prompts, model responses, tool calls, and token usage involved. Sentry shows you the error. Logfire shows you the error and the entire execution that led to it. **Does Logfire have real-time monitoring?** Yes. Logfire has a unique 'pending spans' feature that shows what's happening right now, not just after a request completes. Watch agents run in real-time, see where they're spending time, debug performance issues as they occur. Sentry shows you errors after they happen. **How does AI/LLM support compare?** Sentry has invested in AI observability and can show prompts, token counts, and tool call errors, primarily in the context of what went wrong. Logfire was built for AI applications — one function call gives you the complete LLM trace including prompts, model responses, token usage, cost, and tool calls, whether or not an error occurred. The practical difference is that Logfire surfaces slow prompts or misbehaving tool calls before they become Sentry alerts. **Can I run both Logfire and Sentry?** Yes. Many teams run both: keep Sentry for error alerting and issue tracking, and Logfire for full AI observability and full-stack trace context. You can start with Logfire for AI/LLM monitoring and expand from there, or use Logfire for everything and keep Sentry for specific workflows. The two tools aren't in conflict; they answer different questions. ## Ready to switch from Sentry? Get started with 10 million free spans per month. No credit card required. Start free: https://logfire.pydantic.dev/ — [View pricing](https://pydantic.dev/pricing) --- See also: [Logfire alternatives](https://pydantic.dev/logfire/alternatives) ([markdown](https://pydantic.dev/logfire/alternatives.md)), [Pydantic Logfire](https://pydantic.dev/logfire) ([markdown](https://pydantic.dev/logfire.md)), and [pricing](https://pydantic.dev/pricing.md).