Langfuse vs LangSmith
Langfuse and LangSmith are both evaluated in the CIOPages LLM Observability & Evaluation Buyer's Guide. The guide groups Langfuse under Open Source and LangSmith under Framework-Tied.
| Attribute | Langfuse | LangSmith |
|---|---|---|
| Approach | Open Source | Framework-Tied |
| Best for | Engineering-first and privacy-sensitive teams that want an open, self-hostable platform with no vendor lock-in on telemetry | Teams building on LangChain / LangGraph that want native instrumentation and the least integration work to get a closed eval loop |
From the LLM Observability & Evaluation Buyer's Guide, updated June 2026. 8 vendors evaluated.
Langfuse
Open source with almost nothing held back: the most popular open-source LLM-engineering platform, with nearly its full feature set under a permissive license — tracing, prompt management, LLM-as-judge evals, datasets, and dashboards — framework-agnostic through OpenTelemetry and broad SDKs, and self-hostable in a VPC or on-prem with internet access optional. Self-hosting means you run and scale Postgres, ClickHouse, and object storage yourself. ClickHouse acquired it in early 2026, likely a near-term tailwind for scale but a roadmap dependency to track, and the managed cloud meters by usage.
LangSmith
The tightest trace-to-eval loop if you build on LangChain or LangGraph, though it is now framework-agnostic through multi-language SDKs and OpenTelemetry ingest: strong offline and online evals, multi-turn agent evaluation, cost aggregation across the full workflow, prompt management, and a large community pulled in by the framework. The heritage and best fit remain LangChain-centric, so the value is greatest inside that ecosystem, and it is SaaS-first with usage-based trace pricing, where self-hosting is an enterprise-tier option rather than the default.
Also evaluated in this guide
Arize Phoenix, Braintrust, Comet Opik, Datadog LLM Observability, Galileo, Patronus AI
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