Arize AI is an AI observability and evaluation platform designed to continuously improve AI Agents through agent observability, evaluation, tracing, and experimentation. Its official positioning is as an "agent observability and evaluation platform," with the goal of "turning production signals into continuous learning so agents improve with every interaction."
Purpose
Arize is used to understand and improve modern AI applications, including chatbots, RAG systems, copilots, and agents. Teams can use it to understand the behavior of AI agents and applications, measure quality through evaluations, monitor production performance, and continuously improve prompts, models, and workflows. The platform emphasizes "don't ship on vibes," providing AI teams with observability and evaluation capabilities.
Main Capabilities
- Observe: Trace what agents actually do. Built by the founding team of OpenInference, the leading open standard for GenAI observability.
- Evaluate: Determine whether agents are getting better or worse. It offers one of the more comprehensive eval frameworks on the market, supporting large-scale evaluations at the span, trace, and session levels.
- Learn: Verify whether fixes actually work. Test prompts and harnesses faster before deploying to production.
- Alyx: An in-app AI engineering agent, similar to Cursor or Claude Code but for AI engineering, capable of running evaluations, debugging issues, and improving agents. The user provides the problem, and it fixes it.
- AI-native: Agent-first debugging for coding agents, supporting agent-native workflows such as Cursor, Claude Code, and OpenCode.
- ADB: A data store for GenAI traces, stored in an open format, with native connectivity to BigQuery, Databricks, or Snowflake through DataFabric.
How to Use
Users can connect their own agent or application to Arize and send their first trace. Traces show what happens inside each request, providing thedata needed to debug, evaluate, and improve agents. When getting started, users can first set up tracing for their agent, and later seek help at any time from the in-app AI engineering agent Alyx. Arize can also be used starting from the terminal.
Integrations and Standards
Arize integrates with more than 40 models, frameworks, and AI tools, including OpenAI, Anthropic, Google, Amazon Bedrock, LangGraph, LangChain, LlamaIndex, CrewAI, OpenAI Agents SDK, DSPy, and more. The platform is built on OpenInference and OpenTelemetry standards and can run on Google Cloud, AWS, Azure, and self-hosted environments.
Product Composition
- Phoenix: A leading open-source AI observability and evaluation platform. Developers use Phoenix to trace AI applications, run evaluations, investigate failures, and improve quality. Built on OpenInference and OpenTelemetry standards, it integrates with OpenAI, Anthropic, LangGraph, LangChain, CrewAI, LlamaIndex, and more. It can run locally or be self-hosted.
- Arize AX: A managed AI engineering platform based on the same open standards, adding managed infrastructure (supported by adb, described as the industry's fastest and most scalable observability data store), advanced agent observability, online evaluation, and continuous improvement workflows.
Use Cases
Suitable for development and production teams of modern AI applications such as chatbots, RAG systems, copilots, and agents, helping them trace, evaluate, monitor, and continuously improve AI systems.
Limitations and Compliance
Official materials do not provide specific pricing, billing plans, or detailed usage steps. Arize supports flexible deployment options and states that it is certified under security and compliance standards such as SOC 2 Type II, ISO 27001, PCI DSS, HIPAA, and GDPR, with data always remaining under the user's control. More information can be found in the Arize Trust Center.
