LangSmith vs Langfuse for Observability

As agents move from prototype to production, you need visibility into what they are actually doing: which tools they call, where they fail, and how much each run costs. LangSmith and Langfuse are two leading observability platforms for LLM and agent applications. Both offer tracing, evaluation, and monitoring, but they differ in licensing, deployment, and ecosystem alignment.

Open Source and Self-Hosting

The most consequential difference is how each platform is licensed and deployed. Langfuse is open source and treats self-hosting as a first-class option, which appeals to teams that want to own their observability data, run in their own environment, or meet strict data-sovereignty and air-gapped requirements. The ability to inspect and host the platform yourself is a core part of its value.

LangSmith is a proprietary, managed offering. Self-hosting it is generally available under enterprise arrangements rather than as a default open-source path. If you are comfortable with a managed service and do not need to run the platform yourself, that is not a drawback; if data control and open licensing are priorities, it weighs in Langfuse's favor.

Framework Fit

LangSmith is built by the team behind LangChain, and its tightest integration is with the LangChain and LangGraph ecosystem. If your stack is built around those libraries, LangSmith offers a deeply native experience for tracing and debugging agents with minimal glue. For an all-in-LangChain shop, that integration is a strong reason to choose it.

Langfuse is more framework-agnostic and API-first, designed to work across different stacks rather than favoring one. Both platforms have embraced open standards such as OpenTelemetry, which has reduced lock-in concerns on both sides and made it easier to send data from a variety of sources. Still, the question of whether you want a platform optimized for one ecosystem or one deliberately neutral remains a meaningful split.

Pricing and Data Ownership

The platforms also differ in how they price and how they treat your data. Langfuse uses unit-based pricing and, with self-hosting available, gives teams more direct ownership of their observability data, which can matter for both cost predictability and governance. The open-source path means you can run it without per-trace fees if you operate the infrastructure yourself.

LangSmith is priced as a managed SaaS, typically around trace volume and retention. That model is straightforward and removes operational burden, but it means your data lives in the managed service unless you pursue enterprise self-hosting. Because pricing details on both platforms change over time, confirm current terms before budgeting.

Choosing Between Them

Choose LangSmith if you are heavily invested in LangChain and LangGraph and want a managed, deeply integrated observability platform with minimal setup. Choose Langfuse if you value open-source licensing, want full self-hosting and data ownership, or need a framework-agnostic tool that fits a diverse stack. Both are credible, enterprise-ready choices, so the decision hinges on your framework, your data requirements, and your preference for managed versus self-hosted operation.