Weave vs. OPIK
Opik & Weave: LLM Evaluation Platform Comparison
Compare Opik and Weave to understand how each platform supports LLM evaluation, observability, and optimization for AI applications.
Feature Comparison: Opik vs. Weave
Opik and Weave both provide solutions for evaluating and monitoring LLM applications, but they differ in scope. Opik is an open-source, framework-agnostic platform built to support the full AI development lifecycle, combining observability, evaluation, and optimization in a single system. Weave is focused on LLM observability and evaluation, with strong tracing and visualization capabilities, particularly for teams already using the Weights & Biases ecosystem.
| Feature | Details | Opik | Weave |
|---|---|---|---|
| Open Source | Open-source and fully transparent with enterprise scalability |  Yes |  No |
| Observability | |||
| AI Application Tracing | Trace context, model outputs, and tools | Yes | Yes |
| Token & Cost Tracking | Visibility into key metrics | Yes | Yes |
| AI Provider, Framework & Gateway 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 LLM evaluation metrics | Yes | Yes |
| Built-In Evaluation Metrics | Out-of-the-box scoring and grading systems | Yes | Yes |
| Multi-modal Evaluation | Evaluation support for image, video and audio within the UI | Yes | Partial |
| Evaluation/ Experiment Dashboard | Interface to monitor evaluation results | Yes | Yes |
| 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 | Partial |
| Human Feedback Tracking | Track annotator insights & scores in production | Yes | Yes |
| Production Monitoring | Monitoring for production LLM apps | 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 | Yes |
| Alerting | Configurable alerts | Yes | No |
| 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 is best for teams developing and iterating on AI systems, not just observing them. It combines observability, evaluation, and optimization into a single workflow, making it easier to debug issues and improve performance over time.
Agent Optimization
Automated built-in optimization capability for prompts, tools, and agent workflows
Advanced Annotation UI
Structured annotation workflows, including queues and assignment for human feedback
Deeper System Visibility
Opik allows for prompt-to-trace linkage, tagging, and environment-level organization
Weave’s Advantages
Weave is a strong choice for teams that prioritize observability and are already using the Weights & Biases ecosystem. It provides tracing capabilities, including agent graph visualization, along with evaluation and experimentation workflows that integrate well with existing pipelines.
Tracing Capabilities
Strong trace visualization, including agent graphs and execution flows
Integration with W&B
Built-in integration with the Weights & Biases ecosystem
Evaluation Support
Solid evaluation and experimentation fundamentals for dataset-driven workflows
“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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