Baseline vs Arize AI

Arize AI and Baseline both help teams tell whether their AI is good enough to ship. The difference is what happens after the score: Arize centers on tracing and observability for engineers, while 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 Arize AI, side by side

How they compare	Baseline	Arize AI
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 LLM-as-judge evaluations (relevance, toxicity, and quality) configured through Phoenix or the SDK.
Scheduled, recurring evaluations	A Schedule re-runs a Rubric on a cadence against a connected System and surfaces regressions automatically.	Evaluates traces and datasets from the SDK or UI; 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.	Offers a prompt playground and prompt management with versioning; prompt changes are user-driven.
Who it's built for	Non-technical and technical teammates share one workspace; Readonly Members can view results without editing.	Developer- and ML-engineer-focused, centered on OpenTelemetry tracing and observability.
Getting started	Free tier with no credit card; create a Rubric in the browser.	Open-source Phoenix plus a free managed tier; see Arize pricing for current limits.