Arize AI

Overview

Arize AI is an observability, evaluation, and improvement platform purpose-built for modern AI systems. It unifies production monitoring for large language models, generative AI agents, traditional machine learning, and computer vision applications. The platform is available as Arize AX, a managed observability and evaluation solution, and Arize Phoenix, an open-source library designed for interactive notebook-based experimentation and tracing. By providing continuous visibility into model behavior, Arize AI enables teams to move from black-box deployments to measurable, explainable, and governable AI operations.

At the core of Arize AI is a set of capabilities for understanding how AI systems perform in real-world conditions. For generative AI, the platform captures end-to-end traces of LLM calls, retrieval-augmented generation (RAG) pipelines, tool use, and agent decision paths, allowing teams to decompose complex workflows into observable steps. It supports evaluation of generation quality, context relevance, and agent behavior through a combination of large language model scoring, ground-truth comparison, and user-defined metrics. For ML and CV use cases, Arize AI monitors model prediction drift, data quality issues, and performance degradation over time, helping teams detect silent failures before they affect end users. The platform also supports experimentation and regression testing, so new prompts, model versions, and retrieval strategies can be assessed side by side in production-like conditions.

By centralizing telemetry and evaluation workflows, Arize AI reduces the time required to identify, understand, and remediate model issues. Teams can search and inspect individual traces, analyze aggregate metrics, and drill down into problematic examples to understand root causes. This approach is valuable throughout the machine learning lifecycle — during development, before deployment, and continuously after release. Moreover, Arize AI bridges the gap between offline experimentation and online production observability, enabling teams to validate changes in controlled environments and then monitor them in live settings. Whether an organization is fine-tuning an open-source model, building a production agent, or maintaining a computer vision pipeline, Arize AI provides the shared context needed for ML engineers, platform teams, and product stakeholders to collaborate effectively. Its open-source offering also lowers the barrier for individual practitioners who need quick insights without committing to a full-managed deployment.

Arize AI is adopted by AI-native companies and enterprise organizations to operationalize responsible AI. It gives teams confidence in production quality by promoting rigorous evaluation, transparent traceability, and continuous improvement. From accelerating root cause resolution and reducing costly model failures to enabling systematic prompt and model iterations, Arize AI is designed to support the scale and complexity of modern AI stacks. The combination of managed product and open-source tooling makes observability and evaluation accessible to everyone from small research teams to large enterprise data science organizations.

Core capabilities

Agent & LLM Observability

End-to-end tracing of LLM calls, agent decision paths, tool use, and RAG retrieval flows in production.

Generative AI Evaluation

Assess generation quality, context relevance, and agent performance using LLM-based scoring, ground-truth comparisons, and custom metrics.

ML/CV Monitoring

Detect prediction drift, data quality degradation, and performance regression in traditional machine learning and computer vision models.

Root-Cause Analysis

Search traces, inspect individual inference examples, and drill down into aggregate behavioral patterns to isolate failure modes.

Experimentation & Improvement

Run side-by-side evaluations of prompts, retrievers, model versions, and agent strategies before full deployment.

Arize Phoenix Open Source

Free and lightweight notebook-native tooling for AI observability, tracing, and evaluation experiments.

Production-Scale Telemetry

Ingest, store, and query large volumes of trace and inference data for real-time insights and historical analysis.

Team Collaboration

Shared visibility across ML, engineering, and product teams to align on quality metrics and continuously improve deployed AI systems.

Use cases

Debugging production incidents in LLM-based chat applications and AI agents

Monitoring and optimizing retrieval-augmented generation (RAG) pipelines

Evaluating and comparing different prompts and model versions before release

Tracking agent tool-use accuracy and step-by-step decision quality

Detecting data drift and silent model degradation in computer vision systems

Building an internal LLM evaluation hub for continuous quality control

Supporting responsible AI initiatives with traceable, auditable model telemetry