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LangSmith vs Helicone vs Langfuse: Side-by-Side Comparison

LLM observability platform comparison — comparing monitoring across langsmith and langfuse

LangSmith vs Helicone vs Langfuse: Side-by-Side Comparison

These three get lumped together as "LLM observability" but they solve the problem from different angles: Helicone is a proxy — one base-URL change logs every call, with caching and rate limiting thrown in. Langfuse is the open-source tracing + evals platform — SDK-instrumented, self-hostable, framework-agnostic. LangSmith is the LangChain-native platform — the deepest traces if you live in LangChain/LangGraph, closed-source. The decision is mostly about *how much you instrument* and *where your data must live*.

At a glance

HeliconeLangfuseLangSmith

Integration modelProxy (change base URL)SDK / decorators / OTelSDK; automatic for LangChain Setup effortMinutesHoursMinutes (LangChain) / hours (other) Open source / self-hostCore OSS, self-hostable✅ fully (popular path)❌ (enterprise self-host exists) Tracing depthRequest-levelFull nested tracesFull nested traces (best-in-class for LangChain) Evals / datasetsBasic scoring✅ strong✅ strong Prompt management✅✅✅ (Hub) ExtrasBuilt-in caching, rate limits, key vault—Deep LangGraph debugging Risk profileProxy in the request pathRun it yourself or their cloudVendor + ecosystem lock-in

The real differentiators

Helicone: observability without touching application code. Point your SDK at Helicone's gateway with your project header and every request/response/latency/cost is logged — across any provider, any framework, even code you don't own. The proxy position also enables response caching (identical prompts served free) and per-user rate limiting. The trade: a third party (or a gateway you must operate) sits in your critical path, and because it sees requests rather than your code, trace depth is shallow — it knows you made 6 LLM calls, not that they formed one agent run with a retrieval step between them. (If you're evaluating proxies broadly, compare the gateway category too: LiteLLM vs Portkey.)

Langfuse: the default when self-hosting or staying neutral. SDK instrumentation (@observe decorators in Python, OTel support) yields real nested traces — agent run → retrieval → LLM calls — plus datasets, LLM-as-judge evals, and prompt versioning. Fully open source with a Docker-compose self-host path that's genuinely used in production, which makes it the standard answer for EU/regulated environments where prompt data can't leave your infra. The trade: you write the instrumentation, and self-hosting means operating ClickHouse and friends.

LangSmith: unbeatable inside the LangChain ecosystem, ordinary outside it. Set two env vars and every chain/agent/LangGraph node is traced with zero code change — debugging a misbehaving LangGraph state machine in LangSmith's trace view is the single best experience in this category. Datasets, evals, and prompt hub are mature. Outside LangChain you instrument manually like anywhere else, and you're buying a closed product from the framework vendor — fine if you're committed to that stack, a real coupling if you're not. (Deeper LangSmith-vs-Langfuse workflow comparison: LangSmith LLM evaluation workflow.)

Decision rules

  • LangChain/LangGraph is your stack → LangSmith (the zero-effort trace quality is worth it).
  • Data must stay on your infra / OSS required → Langfuse.
  • Want logging+cost visibility today without code changes → Helicone; add Langfuse later if you need deep traces.
  • Multi-provider gateway features (cache, rate limits, key management) matter as much as logging → Helicone, or pair a gateway with Langfuse.

    • They also compose: a common production stack is Helicone (or LiteLLM) as the gateway layer plus Langfuse for application-level traces and evals — proxy for the network view, SDK for the semantic view.

    FAQ

    Which is cheapest? All three have workable free tiers; at volume, pricing models differ (per-trace vs per-request vs seats) and change often — model your call volume against current pricing pages rather than trusting a blog table.

    Do I need any of these for a prototype? A logging decorator and a spreadsheet go surprisingly far. Adopt a platform when you start *comparing* runs (evals, regressions, prompt versions) — that's when ad-hoc logging collapses.

    OpenTelemetry? Langfuse and a growing set of tools accept OTel GenAI spans; if you have an existing observability estate (Datadog/Grafana), check the OTel path before adopting a separate silo.

    *Last updated: June 2026.*

    Also available in 中文.