Comparison

Logfire vs Langfuse

Both monitor LLM applications. Logfire gives you complete visibility across your entire stack—AI and infrastructure in one place. When your AI agent fails, see both the LLM trace AND the database error that caused it.

Feature Comparison

Quick comparison

FeatureLogfireLangfuse
FocusFull-stack observabilityLLM-specific observability
LLM Tracing
Token/Cost Tracking
Prompt Playground
Full-Stack Observability
Database/API Tracing
Query InterfaceSQL (PostgreSQL)Custom UI / API
OpenTelemetryNativeExport only
Self-HostingEnterpriseOpen Source
Free Tier10M spansLimited
Python SDKFirst-class (Pydantic team)Good
JavaScript SDKFull SDKGood
Any OTel Language

Key Differences

Why teams choose Logfire

Full-Stack vs AI-Only

Your AI doesn't run in isolation. When an agent fails, is it the LLM, the database, or the API it called? Logfire shows you everything in one trace. Langfuse only sees the LLM part.

OpenTelemetry Native

Logfire is built on OpenTelemetry, the industry standard. Any framework with OTel instrumentation works automatically—no special integration needed. Vercel AI SDK, LangChain, FastAPI all just work. No vendor lock-in.

SQL Query Interface

Query your observability data with standard PostgreSQL SQL. Use familiar tools, no proprietary query language to learn. AI assistants write excellent SQL, making complex analysis easy.

Decision Guide

Which should you choose?

Choose Logfire if...

  • You want AI monitoring AND application monitoring in one tool
  • You have services in multiple languages that need unified tracing
  • You prefer SQL-based querying (AI assistants write excellent SQL)
  • You're building with Pydantic/FastAPI
  • You want exceptional Python integrations

Choose Langfuse if...

  • You only need LLM-specific monitoring
  • You need open-source self-hosting (Logfire self-hosting is enterprise)
  • You want built-in dataset management and eval workflows

FAQ

Common questions

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