Langfuse vs LangSmith
Updated March 9, 2026
Overview
Langfuse is an open-source LLM observability platform that provides tracing, analytics, prompt management, and evaluation for AI applications. It captures detailed traces of LLM calls, supports custom scoring, and integrates with LangChain, LlamaIndex, Vercel AI SDK, and raw API calls. Langfuse can be self-hosted for data privacy or used as a managed cloud service. Its open-source model and generous free tier make it popular with startups and developers.
LangSmith is LangChain's observability and evaluation platform for LLM applications. It provides detailed tracing of every LLM call, chain execution, and agent step—showing inputs, outputs, latency, token usage, and cost. LangSmith includes annotation queues for human feedback, dataset management for evaluation, and regression testing for prompt changes. It's the most comprehensive debugging tool for LangChain-based applications.
Key features
Core capabilities each platform advertises.
- Open-source LLM observability
- Detailed trace and span tracking
- Prompt management
- Evaluation scoring
- Self-hosted and cloud options
- Trace visualization for LLM chains
- Prompt versioning and management
- Evaluation and testing suite
- Dataset management
- Tight LangChain integration
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Fully open-source with MIT license and free for commercial use with no usage limits
- Acquired by ClickHouse in January 2026, providing strong backing and future investment
- Active development with strong community support and comprehensive documentation
- Transparent pricing model based on units rather than complex metric calculations
- Deep integrations with major frameworks like LangChain, OpenAI, and LlamaIndex
Cons
- Recent acquisition by ClickHouse may lead to platform changes and strategic shifts
- Smaller team of 7 employees may impact support capacity
- Enterprise pricing at $3,450-$5,700/month can be expensive for mid-sized teams
Pros
- Deep integration with LangChain framework provides unmatched observability for LangChain applications
- Comprehensive evaluation capabilities with custom metrics and automated regression testing
- Excellent tracing UI with detailed step-by-step execution visualization
- Strong dataset management and annotation workflows for human feedback
Cons
- Tight coupling to LangChain ecosystem limits flexibility for non-LangChain applications
- UI scalability issues reported when working with very large datasets
- Production testing limitations - relies primarily on production logs for test datasets
- SaaS-only deployment with no self-hosting option may not suit all enterprise requirements
Langfuse or LangSmith — which should you choose?
Choose Langfuse if you wantChoose if you want
- Self-hosted LLM monitoring
- Open-source tracing for AI applications
- Prompt versioning and management
- Cost tracking across providers
- Community-driven observability
Choose LangSmith if you wantChoose if you want
- Debugging LangChain and LangGraph applications
- Prompt iteration and A/B testing
- LLM output evaluation and scoring
- Team collaboration on prompt engineering
- Regression testing for LLM apps
Compare Langfuse and LangSmith on your own traffic
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 500+ models through one gateway.