BaselineBaseline vs LangSmith
LangSmith and Baseline both help teams measure whether their AI is good enough to ship. The difference is what happens after the score: Baseline turns each evaluation into a Rubric you re-run on a Schedule and hand to an Optimization Run that improves the prompts for you — so quality keeps climbing without an engineer in the loop.
Why teams choose Baseline
- Score AI outputs against a Rubric your whole team can read — no notebook required.
- Put quality on autopilot: a Schedule re-runs your evaluations and flags regressions before customers do.
- Let an Optimization Run rewrite weak prompts for you, then prove the lift against the same Rubric.
Baseline and LangSmith, side by side
| How they compare | Baseline | LangSmith |
|---|---|---|
| Rubric-based scoring of AI outputs | Weighted criteria authored in the UI; every Eval Run returns one overall score the whole Team can read. | Provides evaluators and LLM-as-judge scoring configured via the SDK or UI. |
| Scheduled, recurring evaluations | A Schedule re-runs a Rubric on a cadence against a connected System and surfaces regressions automatically. | Evaluates logged traces and datasets from the SDK or CI; recurring runs are wired up by the user. |
| Automated prompt optimization | An Optimization Run searches for better prompts and proves the lift against the same Rubric. | Centers on tracing, datasets, and experiments; prompt iteration is user-driven. |
| Who it's built for | Non-technical and technical teammates share one workspace; Readonly Members can view results without editing. | Developer-focused, tightly integrated with the LangChain ecosystem. |
| Getting started | Free tier with no credit card; create a Rubric in the browser. | Free tier available; see LangSmith pricing for current limits. |