Langfuse vs LangSmith

Verdict

Langfuse and LangSmith both trace LLM application calls, manage prompt versions and run dataset-based evaluations with human, custom, or LLM-as-judge scoring — the core workflow is close to identical on paper. The decision usually comes down to two things: framework lock-in and self-hosting.

LangSmith is built by the LangChain team and captures traces natively from LangChain and LangGraph applications, which is a real advantage if that's your stack and a non-factor if it isn't — it works with any instrumented app, but the tightest integration is with its own framework. It bills on LangChain Compute Units and Storage Units on top of a per-seat platform fee, and self-hosted or hybrid deployment is reserved for custom-quoted Enterprise plans. Langfuse is open source (MIT license) and free to self-host indefinitely via Docker Compose or Kubernetes with no usage limits beyond your own infrastructure — the hosted Langfuse Cloud runs the identical software, metered by monthly "observability units," letting a team start self-hosted and move to managed cloud later without changing tooling.

Choose LangSmith if

• You're already building on LangChain or LangGraph and want tracing with no extra integration work.
• Your evaluation needs include LLM-as-judge scoring integrated with the same vendor building your orchestration framework.
• You're comfortable with usage-based compute/storage billing rather than a flat per-unit meter.

Choose Langfuse if

• You want to self-host for free, indefinitely, with no seat or usage cap — only your own infrastructure limits you.
• Your stack isn't LangChain-based, or you want to stay framework-agnostic.
• You want the option to start self-hosted and move to a managed cloud plan later without switching tools.

What they share

Both support LLM observability fundamentals — distributed tracing across chains and agent steps, cost and latency tracking, and dataset-based regression testing — and both offer a genuinely free tier (LangSmith's Developer tier includes a monthly trace allowance; Langfuse's self-hosted core has no limit at all). Both list Helicone among their alternatives, reflecting how much this category overlaps at the tracing layer.

The honest caveat

"Open source" and "free" aren't the same thing here: Langfuse's self-hosted deployment still needs infrastructure and someone to operate it, which is a real cost even without a licence fee. And LangSmith's usage-based LCU/LSU pricing is harder to estimate in advance than Langfuse Cloud's flat observability-unit tiers — get a usage estimate from LangSmith based on your actual expected trace volume before comparing sticker prices.