LangSmith vs Helicone
An honest, context-aware comparison. No affiliate links. No paid placements. Just the data that helps you decide.
LangSmith
The observability platform from LangChain — tracing, eval, and prompt management for LLM apps.
Helicone
LLM observability proxy — one line of code to monitor costs, latency, and quality across all AI calls.
Side-by-Side Comparison
Objective metrics, no spin.
Any team building on LangChain or LangGraph — LangSmith pairs seamlessly with no code changes. The path of least resistance.
Teams not using LangChain — Langfuse is more framework-agnostic and has a better self-hosted story for non-LangChain stacks.
Startups and solo developers wanting instant LLM observability without installing an SDK. The fastest path from zero to monitored AI calls.
Teams needing deep tracing of multi-step agent workflows — Langfuse offers more granular observability.
Shared Integrations (2)
Both tools connect to these — you won't lose workflow continuity whichever you pick.
Both suited for: small, medium companies
Since both tools target small and medium companies, your decision should hinge on the specific use case above rather than company fit. Try the AI Advisor to get a recommendation tailored to your exact stack.
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Other AI Observability & MLOps Tools to Consider
If neither is the right fit, these are the next best alternatives in the same category.
Weights & Biases
freeThe MLOps platform for tracking, visualizing, and optimizing ML experiments and model training.
Langfuse
freeOpen-source LLM engineering platform — trace, evaluate, and debug your AI application in production.
Braintrust
starterEnterprise LLM eval platform — logging, evals, and prompt iteration with strong offline scoring.