Arize AI

Arize AI is an enterprise-grade AI and agent engineering platform designed to help teams develop, monitor, and evaluate machine learning models and LLM-based applications in production. Built around two core offerings — the commercial Arize AX platform and the open-source Phoenix project — it provides end-to-end visibility into how AI systems behave once deployed.

The platform addresses a fundamental challenge in modern AI development: the gap between how a model performs in testing versus how it behaves in production. Arize AX covers both the generative AI and traditional ML/CV observability use cases, making it one of the more comprehensive solutions in the LLMOps space. Teams at companies like DoorDash, Uber, Reddit, Roblox, Instacart, and Booking.com rely on it to keep their AI systems reliable at scale.

At its core, Arize provides tracing and observability for AI agents and LLM pipelines, allowing engineers to inspect individual traces, identify failure modes, and detect regressions before they affect end users. The platform includes evaluation tooling — both automated LLM-as-a-judge evals and human review workflows — so teams can systematically measure quality rather than relying on intuition.

Arize AX is split into two product lines. The generative AI platform targets teams building with LLMs and AI agents, offering prompt management, evaluation frameworks, and production monitoring. The ML and CV observability product serves data science teams maintaining traditional models, with tools for detecting data drift, model degradation, and performance regression over time.

Phoenix, the open-source counterpart, can be self-hosted and integrates with the broader Python AI ecosystem. It is particularly popular among teams that want local tracing and evaluation during development before committing to a managed platform.

Compared to alternatives like LangSmith (focused on LangChain ecosystems), Weights & Biases (stronger on training and experiment tracking), or Datadog's LLM observability (infrastructure-first), Arize occupies a position focused specifically on post-deployment AI quality. Its evaluation capabilities — especially the LLM Evals Hub and agent evaluation tooling — are more purpose-built than general APM tools that have added LLM features as an afterthought.

The platform also includes Alyx, an AI engineering agent designed to assist with debugging and optimization tasks within the Arize environment.

Arize targets engineering teams that are past the prototype stage and need structured, systematic approaches to maintaining AI quality in production. The combination of observability, evaluation, and an OSS option makes it a practical choice for organizations at different stages of AI maturity.