Logfire vs Langfuse¶
Both Logfire and Langfuse help you observe AI/LLM applications, but they take fundamentally different approaches. Langfuse focuses specifically on LLM tracing, while Logfire is an AI-native full-stack observability platform.
Quick Comparison¶
| Feature | Logfire | Langfuse |
|---|---|---|
| Focus | Full-stack observability | LLM-specific observability |
| Language Support | Python, JS/TS, Rust SDKs + any OTel | Python, JS/TS SDKs |
| Query Language | SQL (Postgres-compatible) | Custom UI / API |
| Self-hosting | Enterprise option | Open source, self-hostable |
| Pricing Model | Per-span ($2/million)* | Per-event + usage-based |
| Python Support | First-class (Pydantic team) | Good |
| Non-AI Tracing | Full support | Limited |
| LLM Features | Token tracking, costs, panels | Token tracking, costs, evals, prompt mgmt |
| OpenTelemetry | Native | Export support |
*Logfire Cloud pricing (Team or Growth plans). Enterprise pricing available on request.
When to Choose Logfire¶
- Unified observability: You want AI monitoring and application monitoring in one tool
- Built for scale: You need to handle massive data loads — SaaS or self-hosted
- SQL-based analysis: You prefer querying with familiar SQL (and AI assistants write excellent SQL)
- Minimal integration effort: You want to add tracing with minimal code changes
- Pydantic/FastAPI stack: You're already using Pydantic or FastAPI
- Polyglot architecture: You have services in multiple languages that need unified tracing
When to Choose Langfuse¶
- Lighter workloads: You have moderate tracing volumes and performance isn't critical
- Self-hosting priority: You need open-source self-hosting (Logfire self-hosting is enterprise)
- LLM-only focus: You only need LLM tracing and don't want broader observability features
Key Differences Explained¶
Full-Stack vs AI-Only¶
Langfuse focuses specifically on LLM observability: tracing LLM calls, managing prompts, running evaluations.
Logfire provides native AI observability with full-stack monitoring support. When your AI agent makes a database call that fails, you see both the LLM trace AND the database error in one view.
This matters because AI applications don't exist in isolation. They call APIs, query databases, process files. Unified observability means faster debugging.
Query Language — Essential for Agentic Coding¶
Langfuse uses a custom UI and API for querying data.
Logfire uses SQL with PostgreSQL-compatible syntax. This is a significant advantage for AI-assisted development:
- No artificial limitations — Ask any question, get any answer
- AI assistants excel at SQL — GPT-5, Claude, and coding agents write excellent SQL
- Agentic workflows — When coding agents debug your AI application, they can write arbitrary queries to understand and debug production behavior
- Familiar syntax — all in SQL, no new query language to learn
When you're iterating on AI applications with coding agents, the agent needs to understand production behavior. With SQL, it can ask any question. With custom APIs or DSLs, it's constrained to anticipated queries.
Pricing Model¶
Langfuse Cloud charges per event plus usage-based pricing.
Logfire Cloud (Team and Growth) Free tier includes 10M traces. Plans start at $49/month, then charges $2 per million spans after the free tier (10M spans/month). Simple and predictable.
Integration Comparison¶
Both support major LLM providers and frameworks:
| Integration | Logfire | Langfuse |
|---|---|---|
| OpenAI | ✅ | ✅ |
| Anthropic | ✅ | ✅ |
| LangChain | ✅ | ✅ (native) |
| LlamaIndex | ✅ | ✅ |
| Pydantic AI | ✅ (native) | ✅ |
| FastAPI | ✅ | ❌ |
| Next.js | ✅ (via JS SDK) | ❌ |
| Express | ✅ (via JS SDK) | ❌ |
| PostgreSQL | ✅ | ❌ |
| Redis | ✅ | ❌ |
Logfire's broader integration set reflects its full-stack, polyglot approach.
Migration¶
If you're currently using Langfuse and considering Logfire:
- Both are OpenTelemetry-compatible, so instrumentation concepts transfer
- Logfire's Pydantic AI integration is a drop-in replacement
- Consider running both during transition
Summary¶
Choose Logfire if you want AI observability with full-stack monitoring across your entire stack with excellent support.
Choose Langfuse if you specifically want LLM-focused tooling and prefer open-source self-hosting.