Arize Phoenix

Phoenix provides specialized evaluation tools that use LLM-as-a-judge metrics to score outputs for groundedness and relevance. By tracing the execution path, developers can pinpoint exactly where a model deviated from the provided context, allowing for rapid debugging of hallucination issues in RAG pipelines.

Yes, Phoenix includes a powerful embedding visualizer that allows you to project high-dimensional vector data into a 2D or 3D space. This helps teams identify clusters of problematic queries, visualize data drift, and understand how their retrieval systems are performing in real-time.

The migration path is straightforward because both versions share the same core API and data structures. Users can typically transition by updating their endpoint configuration and API keys, allowing teams to start for free locally and scale to the cloud as their production needs grow.

To get started, you simply need to install the phoenix library and initialize the tracer in your script. Once active, Phoenix automatically captures spans and traces from your LlamaIndex queries, providing an instant web-based UI to inspect your retrieval and generation steps.

Phoenix offers seamless, one-click integration with popular frameworks including LangChain, LlamaIndex, and the OpenAI SDK. It uses OpenTelemetry-based tracing, which means it can also be integrated into custom-built Python applications with minimal code changes.

Absolutely. Phoenix is designed to be used across the entire lifecycle, allowing you to run evaluation suites as part of your pre-deployment testing. You can programmatically trigger evaluations to ensure that new prompt versions or model updates meet your quality benchmarks before hitting production.

The Self-Hosted version is an open-source tool designed for local development and full data control. The AX Pro plan, priced at $50/month, is a managed SaaS offering tailored for small teams who need hosted infrastructure, persistent storage, and collaborative features without managing their own servers.

Yes, the AX Enterprise plan offers both a managed SaaS option and a dedicated Self-Hosted deployment. This plan is designed for large organizations requiring advanced security, custom SLAs, and the ability to run the observability platform within their own VPC or private cloud environment.

Phoenix provides tools for data masking and PII redaction to ensure sensitive information is not stored or visualized. For organizations with the highest security needs, the Self-Hosted and Enterprise versions allow you to keep all trace data within your own infrastructure, ensuring it never leaves your perimeter.

Arize AI maintains a strong commitment to security, and our managed AX platforms are built to meet enterprise standards. For specific compliance documentation such as SOC 2 Type II reports or GDPR data processing agreements, customers on the Enterprise plan can request our full security package.

AX Pro users receive standard email support and access to our community forums. Enterprise customers benefit from dedicated support channels, including Slack integration, prioritized ticket handling, and architectural reviews with our AI observability experts to optimize their monitoring setup.

Comprehensive documentation, API references, and step-by-step tutorials are available at phoenix.arize.com. We also provide a library of pre-built evaluation templates and Jupyter notebooks to help you implement best practices for monitoring RAG and agentic workflows.