Baseline vs Langfuse

Langfuse and Baseline both help teams measure AI quality. The difference is what you do with the result: 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 babysitting it.

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 Langfuse, side by side

How they compare	Baseline	Langfuse
Rubric-based scoring of AI outputs	Weighted criteria authored in the UI; every Eval Run returns one overall score the whole Team can read.	Records LLM-as-judge and custom scores against traces, configured by the user.
Scheduled, recurring evaluations	A Schedule re-runs a Rubric on a cadence against a connected System and surfaces regressions automatically.	Supports evaluations on traces and datasets; cadence is configured 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 and open-source, with self-hosting available.
Getting started	Free tier with no credit card; create a Rubric in the browser.	Open-source with a free cloud tier; see Langfuse pricing for current limits.