Data & ML OpsFreemiumReviewed June 2026

LangSmith

LangSmith is what you wish you'd added before your first production AI incident. It traces every LLM call in your app — prompts, tool calls, retries, latencies, token spend — and lets you run evaluations against curated datasets to catch regressions before they ship. Works with any framework (LangChain, LangGraph, raw OpenAI / Anthropic SDKs), not just LangChain proper. The free tier handles small teams; paid tiers scale by traces and seats. For Custom Deployments work, LangSmith is non-optional.

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What it's good for

1
Tracing every production LLM call so debugging a "weird output" takes minutes, not hours
2
Catching regressions before they ship — evaluate prompt changes against a golden dataset
3
Cost monitoring — see exactly where tokens are spent and which user or feature is expensive
4
Comparing prompt variants in production with the built-in A/B framework
5
Collecting human feedback (thumbs, scores, annotations) and feeding it into eval datasets

How to use it

Wrap your LLM client with the LangSmith SDK (one decorator in Python, one wrapper in JS) and every call is automatically traced. Tag traces with metadata so you can filter by user, feature, or environment. Build eval datasets from real traces — promote interesting examples into a labelled set, then run automated evals (LLM-as-judge, exact match, or custom Python) against new prompt versions. The free tier covers 5K traces / month; serious teams quickly outgrow that.

LangSmith docs

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LangSmith

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Alternatives to LangSmith

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