Langfuse vs LangSmith
Detailed side-by-side comparison to help you choose the right tool
Langfuse
🔴DeveloperBusiness Analytics
Open-source LLM engineering platform for traces, prompts, and metrics.
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FreeLangSmith
🔴DeveloperBusiness Analytics
Tracing, evaluation, and observability for LLM apps and agents.
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FreeFeature Comparison
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Langfuse - Pros & Cons
Pros
- ✓Fully open-source with self-hosting that has complete feature parity with the cloud version
- ✓Hierarchical tracing captures the full execution tree of complex agent workflows, not just LLM calls
- ✓Prompt management with versioning and production linking creates a tight iteration feedback loop
- ✓Native integrations with LangChain, LlamaIndex, OpenAI SDK, and Vercel AI SDK require minimal code changes
- ✓Evaluation system supports both automated LLM-as-judge scoring and human annotation queues
Cons
- ✗Dashboard analytics are functional but less polished than commercial observability platforms for executive reporting
- ✗UI performance degrades noticeably with very large trace volumes (millions of traces)
- ✗ClickHouse dependency for self-hosting adds operational complexity compared to PostgreSQL-only setups
- ✗Documentation can lag behind feature releases, especially for newer evaluation and dataset features
LangSmith - Pros & Cons
Pros
- ✓Comprehensive observability with detailed trace visualization
- ✓Native MCP support for universal agent tool deployment
- ✓Generous free tier for individual developers and small projects
- ✓No-code Agent Builder reduces technical barriers
- ✓Managed deployment infrastructure with production-ready scaling
- ✓Strong integration with entire LangChain ecosystem
Cons
- ✗Primarily designed for LangChain applications (limited framework support)
- ✗Steep pricing jump from Plus to Enterprise tier
- ✗Pay-as-you-go model can become expensive for high-volume applications
- ✗Enterprise features require annual contracts
- ✗14-day retention on base traces may be insufficient for some use cases
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