Phoenix — Observability, Prompts & Evals Platform
Observability, Prompts & EvalsLayer 4Open SourceWhat is Phoenix?
Phoenix is the open-source observability and evaluation platform built by Arize AI for LLM and agent applications. It is OpenTelemetry-native, which means traces written through Phoenix can flow into any OTel-compatible backend in addition to Phoenix's own UI. The platform includes built-in evaluators for hallucination detection, retrieval relevance, and QA correctness, plus dataset management and prompt playground features. Phoenix can be deployed via Docker for self-hosting or used in Arize's managed cloud. The open-source core makes it attractive to teams that want to inspect and customize the observability layer, while the integration with the full Arize platform provides an upgrade path for organizations that need enterprise features like RBAC, SSO, and SLA-backed support.
Key Features
- ✓OpenTelemetry-based LLM and agent tracing
- ✓Built-in evaluators for hallucination and relevance
- ✓Dataset and experiment management
- ✓Prompt playgrounds and versioning
- ✓Self-hosted Docker deployment or Arize cloud
Pros & Cons
Pros
- +Open-source with active development by Arize
- +OpenTelemetry-native (no proprietary trace format lock-in)
- +Strong evaluator library out of the box
- +Both self-hosted and managed cloud options available
- +Upgrade path to full Arize enterprise platform
Cons
- -Smaller community than Langfuse for open-source-first teams
- -Cloud version's enterprise pricing is contact-sales only
- -UI and feature set tilt toward ML engineers more than application developers
Phoenix Pricing
Common Use Cases
Engineering teams building agent and RAG systems who want OpenTelemetry-native observability with both self-hosted and managed options
- •OpenTelemetry-native LLM observability
- •Hallucination detection for RAG
- •Experiment tracking and golden-set evaluation
- •Prompt iteration and comparison
- •Self-hosted tracing for compliance-sensitive teams
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