Comparison
Logfire vs Langfuse
Both monitor LLM applications, but differ on scope and architecture. Langfuse is LLM-specific while 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
| Feature | Logfire | Langfuse |
|---|---|---|
| Observability scope | Full-stack: AI, databases, APIs, and infrastructure in one trace | LLM-specific observability |
| LLM Tracing | ✓ | ✓ |
| Token/Cost Tracking | ✓ | ✓ |
| Prompt Playground | ✓ | ✓ |
| Full-Stack Observability | ✓ | ✗ |
| Database/API Tracing | ✓ | ✗ |
| Query Interface | SQL (PostgreSQL) | Custom UI / API |
| MCP server | Query production traces and spans from your editor or AI agent | Prompt management only |
| OpenTelemetry | Native; fully portable instrumentation | Export only |
| Self-Hosting | Enterprise | Open Source |
| Free Tier | 10M spans | Limited (each trace, span, eval score counts separately) |
| Python SDK | First-class (Pydantic team) | Good |
| JavaScript SDK | Full SDK | Good |
| Any OTel Language | ✓ | ✗ |
Cost Savings
Pricing comparison
| Workload | Langfuse | Logfire | Savings |
|---|---|---|---|
| 1 user, 5M spans/mo | ~$451 | $0 (free tier) | 100% |
| 5 users, 50M spans/mo | ~$3,451 | ~$129 | ~27x |
| 20 users, 500M spans/mo | ~$36,801 | ~$1,229 | ~30x |
*Logfire Team or Growth plans (base + $2/million spans). Langfuse Core Plan ($29/mo base + $8/100k units); units count every trace, observation, and evaluation score separately.
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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