Keywords AI

Langfuse vs LangSmith

Compare Langfuse and LangSmith side by side. Both are tools in the Observability, Prompts & Evals category.

Quick Comparison

	Langfuse	LangSmith
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Open Source	Freemium
Best For	Teams who want open-source LLM observability they can self-host and customize	LangChain developers who need integrated tracing, evaluation, and prompt management
Website	langfuse.com	smith.langchain.com
Key Features	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
Use Cases	Self-hosted LLM monitoring Open-source tracing for AI applications Prompt versioning and management Cost tracking across providers Community-driven observability	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

When to Choose Langfuse vs LangSmith

Choose Langfuse if you need

Self-hosted LLM monitoring
Open-source tracing for AI applications
Prompt versioning and management

Pricing: Open Source

Choose LangSmith if you need

Debugging LangChain and LangGraph applications
Prompt iteration and A/B testing
LLM output evaluation and scoring

Pricing: Freemium

About Langfuse

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.

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About LangSmith

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.

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What is Observability, Prompts & Evals?

Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.

Browse all Observability, Prompts & Evals tools →