Full-stack, not 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.
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 | Logfire | Langfuse |
|---|---|---|
| Observability scope | Full-stack: AI, databases, APIs, and infrastructure in one trace | LLM-specific observability |
| LLM Tracing | Yes | Yes |
| Token/Cost Tracking | Yes | Yes |
| Prompt Playground | Yes | Yes |
| Human annotations & queues | Annotations on production runs + annotation queues | Annotations on production runs + annotation queues |
| Full-Stack Observability | Yes | No |
| Database/API Tracing | Yes | No |
| Query Interface | SQL (PostgreSQL) | Custom UI / API |
| MCP server | SQL queries over production telemetry from your editor or AI agent | Query observations, metrics, scores, datasets, and annotation queues |
| OpenTelemetry | Native; fully portable instrumentation | Export only |
| Self-Hosting | Enterprise | Open Source |
| Free Tier | 10M logs, spans, and metrics | Limited (each trace, span, eval score counts separately) |
| Python SDK | First-class (Pydantic team) | Good |
| JavaScript SDK | Full SDK | Good |
| Any OTel Language | Yes | No |
| 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.
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.
Logfire is built on OpenTelemetry, the industry standard. Any framework with OTel instrumentation works automatically, with no special integration needed. Vercel AI SDK, LangChain, FastAPI all just work. No vendor lock-in.
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.
Logfire provides full-stack observability covering your entire application (AI, databases, APIs), while Langfuse focuses specifically on LLM tracing. When your AI agent fails, Logfire shows you both the LLM trace AND the database error or API timeout that caused it.
Logfire offers a free tier of 10M logs, spans, and metrics a month, then $2 per million records. Langfuse uses per-event pricing plus usage-based costs. The best choice depends on your specific usage patterns and whether you need full-stack observability or LLM-only monitoring.
Yes. Logfire provides auto-instrumentation for LangChain, LlamaIndex, OpenAI, Anthropic, and many other AI frameworks. You can visualize complex chains and retrieval flows automatically.
Yes. Both platforms are OpenTelemetry-compatible, so instrumentation concepts transfer easily. Logfire's Pydantic AI integration is a drop-in replacement. You can run both during a transition period.
Logfire's SDK is open source. The platform is available as cloud-hosted or self-hosted (enterprise). If open-source self-hosting is a hard requirement, Langfuse may be a better fit.
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