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Logfire vs Sentry

Sentry is a mature error monitoring platform. Logfire is an AI-native observability platform built on OpenTelemetry with full-stack monitoring capabilities. While Sentry excels at catching and tracking errors, Logfire provides complete visibility into your application's behavior, AI to API.

Quick Comparison

Feature Logfire Sentry
Primary Focus Full observability (logs, traces, AI) Error monitoring
App Tracing Core capability Available, not a core focus
AI/LLM Support First-class, automatic instrumentation Generic function tracing only
Logging Structured logs with full context Error-focused
Live View Real-time "pending spans" ❌
Query Interface SQL (Postgres-compatible) Custom UI
Pricing 10M spans free, then $2/M Per-event + quotas
*Logfire Cloud pricing (Team or Growth plans). Enterprise pricing available on request.

When to Choose Logfire

  • Full observability: You want logs, traces, AND error tracking in one tool
  • AI/LLM applications: You need to observe prompts, responses, token usage
  • Real-time debugging: You want to see what's happening right now, not just errors
  • Frontend + backend debugging: Use Logfire's JavaScript SDK to trace and debug your entire application
  • SQL analysis: You want to query your data with familiar SQL
  • Unified tooling: You don't want to juggle Sentry + logging service + APM tool
  • One-click AI-assisted debugging via MCP

When to Choose Sentry

  • Error-focused workflow: Your primary need is catching and triaging errors
  • Frontend error tracking: You need robust JavaScript error tracking with source maps
  • Issue tracker integration: You rely on deep integrations with Jira, GitHub Issues, etc.

Key Differences Explained

Complete Observability vs Just Errors

Sentry captures exceptions and performance issues. It's excellent at what it does, but it only shows you when things go wrong.

Logfire provides full observability:

  • Structured logs: Every log/span/trace with full context, not just errors
  • Issue alerts: Automatic exception grouping, fingerprinting, and webhook alerts to Slack
  • Distributed traces: See requests flow through your entire system
  • Real-time monitoring: Watch your application in real-time with "pending spans"
  • AI visibility: Automatic instrumentation for LLM calls, tool invocations, and more

AI/LLM Support

Sentry treats AI calls like any other function. You'll see that an error occurred, but you won't see:

  • What prompt was sent
  • What the model responded
  • Token usage and costs
  • Tool calls and their results

Logfire was built for AI applications. One function call gives you complete LLM visibility:

import logfire
logfire.configure()
logfire.instrument_openai()  # That's it

Live Debugging

Sentry shows you errors after they happen.

Logfire has a unique "pending spans" feature that shows you what's happening right now. Watch requests in real-time, see where they're spending time, debug performance issues as they occur.

SQL-Powered Analytics — Essential for Agentic Coding

Sentry uses a custom UI for querying and filtering.

Logfire uses SQL with PostgreSQL-compatible syntax. This is a significant advantage for AI-assisted development:

  • "Show me all FastAPI requests that called our LLM more than 3 times"
  • "What's the average token usage by endpoint?"
  • "Which users are hitting rate limits most often?"

AI assistants and coding agents are excellent at writing these queries. When you're iterating on AI applications with coding agents, the agent needs to understand production behavior. With SQL, it can ask any question—no artificial limitations. With custom UIs or APIs, it's constrained to anticipated queries.

Migration Path

If you're using Sentry and want to add Logfire:

  1. Complementary use: Many teams run both. Sentry for error alerting, Logfire for observability.
  2. Gradual transition: Start with Logfire for AI/LLM monitoring, expand from there
  3. Full migration: Replace Sentry entirely if you want unified tooling

Summary

Choose Logfire for AI/LLM observability including full-stack monitoring support, real-time debugging, and SQL-based analysis.

Choose Sentry if your primary need is error monitoring with mature issue tracking integrations.

Consider both if you want Sentry's error alerting alongside Logfire's AI and broader observability.