Arize AI

✓ Editorially verified

Enterprise observability and evaluation platform for LLM agents and generative AI applications.

Freemium· Free tier and OSS Phoenix; paid/enterprise tiers via salesEvaluationMulti-model

Best for

Pick Arize if you're running LLM agents or RAG in production and need real tracing, evals, and regression testing rather than ad-hoc logging.

Skip if

Skip it if you're a solo builder shipping a side project; the OSS Phoenix tool alone will likely cover your needs.

Arize AI is a production-grade observability and evaluation platform built for teams running LLM agents, RAG systems, and copilots at scale. Its core loop is trace, eval, learn: capture every span of agent behavior on top of the open OpenInference standard, run span/trace/session-level evals, then iterate on prompts and workflows before shipping. The platform also ships Alyx, an AI engineering agent that helps debug failures, plus a purpose-built GenAI trace datastore (adb) that the company says handles roughly a trillion spans and a billion evals per month.

It's aimed squarely at AI engineering teams inside larger orgs, with reference customers like Reddit, DoorDash, Uber, and Spotify, and the usual enterprise checklist of SOC 2 Type II, ISO 27001, HIPAA, and GDPR. Pricing isn't public, but there's a free hosted tier via sign-up and a fully open-source path through Phoenix, Arize's self-hostable observability and eval tool, which makes it one of the few eval vendors with a credible OSS on-ramp.

Integrations cover 40+ tools across the GenAI stack: OpenAI, Anthropic, LangGraph, LangChain, CrewAI, and the three major clouds. Self-hosted deployment is supported alongside the managed SaaS, which matters for regulated workloads.

Editor's take

Arize is the most credible end-to-end eval and observability stack for agentic systems right now, and the Phoenix OSS layer gives teams a low-risk way to start. The trade-off is enterprise gravity: pricing is gated, and the full platform really shines once you have meaningful traffic and a team that cares about regressions.

— The AI Tool Bible editorial team