Observability and evaluation platform for machine learning and LLM apps
Arize is an observability platform for machine learning models and language model applications. For classic models it tracks drift, data quality, performance decay and feature importance once a model is live, which is precisely the stage where most monitoring stops. For language model work it covers tracing, prompt and response evaluation, hallucination and relevance checks, cost tracking and retrieval quality. Phoenix, its open-source companion, offers much of the evaluation and tracing capability for local use. The value is diagnostic: without this layer, teams discover a degraded model or a bad prompt from customer complaints rather than from data.
Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.
ml monitoring, llm evaluation, drift detection and production debugging
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Once something is serving real users. Before that, evaluation in development is enough. After launch you need to know whether inputs drifted, whether quality decayed and which prompt version caused a regression.
You log inputs, outputs and metadata for each request, usually through the SDK or a framework integration. That is a small amount of code, but deciding what to capture and how to label it is the hard part.
It supplies the framework, graders and dashboards; you supply the criteria and reference data. Judging whether an answer is good for your domain remains a human design decision that no platform can make on your behalf.
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