Phoenix vs. OPIK
Opik & Phoenix: LLM Evaluation Platform Comparison
Compare how Opik and Phoenix support evaluation, observability, and agent workflows across development and production.
Opik vs. Phoenix Feature Comparison
Opik and Phoenix are two open-source platforms that aim to improve LLM applications, but they focus on different layers of the GenAI stack. Phoenix centers on open-source tracing, embedded visualization, and RAG debugging, making it ideal for early-stage experimentation and observability. Opik provides a broader, end-to-end platform spanning evaluation, human feedback, optimization, and production monitoring, giving teams full lifecycle coverage from development to deployment.
| Feature | Details | Opik | Phoenix |
|---|---|---|---|
| Observability | |||
| AI Application Tracing | Trace context, model outputs, and tools |  Yes | Yes |
| Multi-Modal Evaluation | Evaluation support for images & videos | Yes | Partial |
| Token & Cost Tracking | Visibility into key metrics | Yes | Yes |
| AI Framework Integrations | Native integrations with model providers & various frameworks | Yes | Yes |
| OpenTelemetry Integration | Native support with OpenTelemetry | Yes | Yes |
| Evaluation | |||
| Custom Metrics | Create your own LLM-as-a-Judge, or criteria-based metrics for evaluation | Yes | Yes |
| Built-In Evaluation Metrics | Out-of-the-box scoring and grading systems | Yes | Yes |
| Evaluation/ Experiment Dashboard | Interface to monitor evaluation results | Yes | Yes |
| Automated Dataset Expansion | Automatically expand datasets for robust evaluation | Yes | Partial |
| Agent Evaluation | Evaluate complex AI apps and agentic systems | Yes | Yes |
| Evaluation and Human Feedback for Conversations | Track annotator insights & scores in production | Yes | Yes |
| Annotation Queues | Review and annotate outputs by subject matter experts | Yes |  No |
| Human Feedback Tracking | Track annotator insights & scores in production | Yes | Partial |
| Production Monitoring | Monitoring for production LLM apps | Yes | No |
| Prompt Playground | Test & refine prompts and outputs from LLMs | Yes | Yes |
| Agent Optimization | |||
| Automated Agent Optimization | Automatically refine entire agents & prompts | Yes | No |
| Tool Optimization | Optimize how agents use tools | Yes | No |
| Production | |||
| Online Evaluation | Score production traces and identify errors within LLM apps | Yes | No |
| Alerting | Configurable alerts | Yes | No |
| TypeScript & JavaScript SDK | Developer SDK for JavaScript and TypeScript | Yes | Yes |
| In-Platform AI Assistant | Embedded assistant to guide workflows | Yes | No |
These Are Just the Highlights
Explore the full range of Opik’s features and capabilities in our developer documentation or check out the full repo on GitHub.
Opik’s Advantages
Opik distinguishes itself as a full-stack evaluation and production-monitoring platform for LLMs and agentic systems. Beyond tracing, Opik offers robust evaluation workflows, human feedback systems, automated optimization, and production-grade reliability tooling, offering a single place to test, validate, improve, and monitor AI applications. Opik is ideal for teams shipping AI products into production and needing a reproducible, scalable evaluation and observability stack.
End-to-End Evaluation
Online evaluation, thread-level scoring, multimodal tests, custom metrics, and dataset expansion.
Powerful Optimization
Automated prompt, tool, and multi-objective optimization.
Human Feedback Workflows
Annotation UI, queues, multi-annotator reviews, conversation-level evaluation.
Production Readiness
Guardrails, advanced observability, alerts, human feedback monitoring, and LLM gateway routing.
Phoenix’s Advantages
Phoenix shines as an open-source LLM observability and debugging toolkit, especially for teams exploring model behavior, RAG pipelines, and embeddings. Phoenix is best suited for teams seeking a lightweight, open-source debugging and tracing experience with rich visualization capabilities.
RAG & Embedding Visualization
Strong support for inspecting retrieval pipelines and embeddings.
Powerful Playground
Tool use, composability, advanced experiment launching, and span replay
Environment & User Tracking
Built-in observability features for differentiating traffic sources.
OpenTelemetry Focus
Advanced OTel ingestion for teams with distributed tracing pipelines.
“Opik being open-source was one of the reasons we chose it. Beyond the peace of mind of knowing we can self-host if we want, the ability to debug and submit product requests when we notice things has been really helpful in making sure the product meets our needs.”
Jeremy Mumford
Lead AI Engineer, Pattern
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