# Langfuse vs LangSmith: LLM Observability Compared (2026)

> Langfuse vs LangSmith — open-source self-hostable observability vs LangChain's first-party platform. Tracing, evals, prompt management, and which to adopt.

Ecosystem and ownership decide it. LangSmith is the first-party choice for LangChain/LangGraph stacks — deepest integration, polished evals, managed SaaS. Langfuse is the open-source, framework-neutral choice — MIT-licensed core, self-hostable for data control, SDKs and OpenTelemetry reach across any stack. Heavy LangChain shops pick LangSmith; everyone else's default tilts Langfuse.

Once an LLM feature ships, the questions change: *what did the model actually do, why did this trace cost $4, which prompt version regressed?* Answering them is observability, and **Langfuse vs LangSmith** is the category's defining matchup — first-party ecosystem depth versus open-source neutrality.

## The short answer

- **Built on LangChain/LangGraph** → **LangSmith**; the integration depth is unmatched and you'll feel it daily.
- **Framework-mixed stack, or traces must stay on your infra** → **Langfuse**; open source, self-hostable, neutral.
- **Genuinely torn** → Langfuse is the lower-regret default: nothing about it punishes you for not using LangChain, and the exit door stays open.

## What each is

**LangSmith** is LangChain's commercial platform: tracing, evals, prompt management, dashboards, and alerting, built by the team whose framework it instruments. Deep LangGraph runs unfold node by node with zero setup; datasets and judge-based experiments plug into the same traces; production monitoring closes the loop. It's managed SaaS (with enterprise self-host options) and proprietary — you're buying polish and proximity. [Tool profile →](/tools/langsmith)

**Langfuse** is the open-source engineering platform for the same job: MIT-core tracing, prompt management with versioning and deployment, eval pipelines (LLM-as-judge, human annotation, datasets), and analytics — framework-agnostic by design, with SDKs (Python/JS) and integrations across the gateway/framework landscape. Self-hosting is first-class, not an enterprise afterthought: your traces, your Postgres/ClickHouse, your compliance story. [Tool profile →](/tools/langfuse)

## Dimension by dimension

| | Langfuse | LangSmith |
| --- | --- | --- |
| Source/ownership | Open source (MIT core), self-host first-class | Proprietary SaaS (enterprise self-host) |
| Framework fit | Neutral (SDKs, OTel-style reach) | LangChain/LangGraph native, others via SDK |
| Tracing depth | Excellent, instrumentation yours | Automatic & deepest on LangChain |
| Evals | Datasets, judges, annotation queues | Datasets, judges, polished experiment UX |
| Prompt management | Versioned, deployable | Versioned, playground-integrated |
| Data control | Total (self-host) | Vendor-managed (mostly) |
| Cost shape | Free OSS + usage SaaS | Free tier + usage SaaS |

## How to actually choose

This is an ecosystem decision disguised as a feature comparison — the feature lists converge more every quarter. **Follow your framework gravity** first: a LangGraph shop forgoing LangSmith is leaving daily ergonomics on the table; a Vercel-AI-SDK-plus-custom-agents shop gains nothing from LangSmith it can't get neutrally. **Then apply the data constraint**: if "LLM traces contain customer data and must not leave our VPC" describes you, Langfuse self-hosted ends the conversation.

Whichever you pick, the observability platform is the *substrate* — the value comes from the [eval discipline you run on it](/guides/evaluation/write-llm-evals) and the [production tracing habits](/agents/data-ai/llm-observability-engineer) that catch regressions before users do. The wider tool field (Phoenix, Braintrust, Helicone, promptfoo) is mapped in [Best LLM & RAG Evaluation Tools in 2026](/guides/evaluation/best-llm-eval-tools-2026).

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_Source: https://agentscamp.com/guides/comparisons/langfuse-vs-langsmith — Guide on AgentsCamp._