langfusevslangsmith

what each one actually is

Langfuse is an open source LLM observability and analytics platform. it provides tracing (see exactly what happened in each LLM call, chain, or agent run), prompt management (version your prompts, A/B test them), evaluations (score outputs with models or humans), and a dashboard for analyzing cost, latency, and quality. it integrates with virtually every LLM framework and runs both as a managed cloud service and on self-hosted infrastructure.

LangSmith is LangChain's official observability and evaluation platform. it's built by the same team as langchain and langgraph, which means the integration is deep and first-class. if you're using langchain expressions (LCEL) or langgraph agents, langsmith picks up traces with almost zero configuration. it has evals, prompt management, dataset management, and annotation queues. it's a managed cloud product — no self-hosting option.

pricing, honestly

langfuse cloud free tier: 50,000 observations/month, 2 users, 30-day data retention. enough to evaluate seriously and run a small production workload. the team plan starts at $59/month for 1M observations and longer retention. for teams with high tracing volume, the self-hosted option eliminates the per-observation cost entirely.

langsmith's pricing shifted upward in 2025. the developer tier is free for limited usage; the plus tier starts at $39/month per seat. for teams with high trace volume and multiple users, langsmith can cost significantly more than langfuse's equivalent tier. langsmith doesn't have a self-host option to escape the cost curve.

what it's actually like to use them

getting started with langfuse is a good benchmark for how LLM tooling should feel: install the SDK, set two environment variables, wrap your LLM call, and traces appear in the dashboard within seconds. the UI shows inputs, outputs, tokens, latency, and cost per trace with clean hierarchical visualization for chain and agent runs. prompt management lets you pull prompts at runtime with version pinning. the dataset and eval tooling is genuinely useful for teams doing systematic quality testing.

langsmith's integration story is remarkable if you're on langchain. a single environment variable (LANGCHAIN_TRACING_V2=true) and langsmith captures every chain, every tool call, every LLM invocation automatically. the trace visualization is purpose-built for langchain's structure — you see exactly which nodes fired in your chain and what data passed through each. if you're not on langchain, you're using the generic SDK, which is fine but not the experience langsmith is designed around.

who langfuse is for

• teams using multiple LLM frameworks (vercel ai sdk, mastra, llamaindex, raw openai, anthropic SDK)
• companies with data residency or compliance requirements who need self-hosted observability
• teams that want to audit their observability tool or contribute to it
• anyone price-sensitive at production trace volumes who can't absorb langsmith's per-seat costs
• teams building evals outside the langchain ecosystem

who langsmith is for

• teams using langchain (LCEL, LangGraph) who want zero-configuration tracing
• organizations building with langchain that want first-party support and tight integration
• teams that don't need self-hosting and want the tightest possible integration with their agent framework

when to avoid each

don't use langfuse if you want zero-config langchain integration and are never going to use another framework. langfuse's langchain callback works but requires one more line of setup than langsmith, which is trivial but worth naming.

don't lock into langsmith if you're mixing frameworks or might want to run your own infrastructure. langsmith's pricing at scale and its closed-source nature make it a risky foundation for teams that expect significant growth in trace volume.

stuff their landing pages won't tell you

• langfuse's self-hosted version ships with the same features as the cloud version — no artificial feature gating
• langsmith's "free tier" is limited in ways that become apparent at real workloads — check the specific limits for your use case
• both tools calculate cost automatically from token counts and model pricing — useful but requires keeping model pricing tables updated
• langfuse has an active community in discord and github; langchain/langsmith support is official but slower for non-enterprise users
• both platforms support human annotation queues for labeling trace outputs — useful for building ground-truth eval datasets
• langfuse's prompt management works at runtime: you update a prompt in the UI and your production app picks it up without a redeploy. langsmith has similar capability.

the call

langfuse. the open source + self-hosting story is the most important consideration for teams that expect significant scale. locking your trace data into a closed-source cloud product before you know your volume is the same mistake teams made with logging before they discovered they couldn't afford the managed provider.

langsmith is a perfectly valid choice if and only if you're committed to langchain as your primary LLM framework. the zero-config integration is a real advantage and the tooling quality is high. but the moment your stack diversifies, langfuse is the more durable foundation.

for a new AI product in 2026: start with langfuse, self-host if your data is sensitive, and revisit langsmith only if you go all-in on langgraph.