Comparison
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.
Feature Comparison
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 |
Key Differences
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.
Decision Guide
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
Common questions
Ready to switch from Sentry?
Get started with 10 million free spans per month. No credit card required.