LangSmith vs Langfuse
An honest, context-aware comparison. No affiliate links. No paid placements. Just the data that helps you decide.
LangSmith
The observability platform from LangChain — tracing, eval, and prompt management for LLM apps.
Langfuse
Open-source LLM engineering platform — trace, evaluate, and debug your AI application in production.
StackMatch Editorial verdicts
Bylined · No vendor influenceThis tool hasn't been reviewed yet by StackMatch Editorial. The data above is what we have so far.
Langfuse is the best-in-class open-source option for LLM tracing, evals, and prompt management. Self-hosting is real, pricing is fair, and the product has outpaced commercial competitors.
Read full review →Side-by-Side Comparison
Objective metrics, no spin.
Any team building on LangChain or LangGraph — LangSmith pairs seamlessly with no code changes. The path of least resistance.
Teams not using LangChain — Langfuse is more framework-agnostic and has a better self-hosted story for non-LangChain stacks.
Every team running LLM applications in production. Langfuse makes debugging, cost tracking, and quality evaluation possible.
Simple prototyping — adds overhead before you have traffic worth monitoring.
Shared Integrations (3)
Both tools connect to these — you won't lose workflow continuity whichever you pick.
Both suited for: small, medium, large companies
Since both tools target small and medium and large companies, your decision should hinge on the specific use case above rather than company fit. Try the AI Advisor to get a recommendation tailored to your exact stack.
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Other AI Observability & MLOps Tools to Consider
If neither is the right fit, these are the next best alternatives in the same category.
Weights & Biases
freeThe MLOps platform for tracking, visualizing, and optimizing ML experiments and model training.
Helicone
freeLLM observability proxy — one line of code to monitor costs, latency, and quality across all AI calls.
Braintrust
starterEnterprise LLM eval platform — logging, evals, and prompt iteration with strong offline scoring.