MLFlow vs. OPIK
Opik & MLflow GenAI: LLM Evaluation Platform Comparison
Compare how Opik and MLflow GenAI support evaluation, observability, and optimization for LLM-powered and agentic applications
Feature Comparison: Opik vs. MLflow GenAI
Opik and MLflow GenAI offer functionality for AI development workflows. MLflow originated as a general-purpose machine learning lifecycle platform focused on experiment tracking and model management, with GenAI support layered in through prompt tracking and SDK-based extensions. Opik is built specifically for LLM-powered and agentic applications, focusing on evaluation, observability, and automated optimization of prompts, tools, and multi-step agent workflows from development through production.
| Feature | Details | Opik | MLflow GenAI |
|---|---|---|---|
| Open Source | Open-source and fully transparent with enterprise scalability |  Yes | Yes |
| Observability | |||
| AI Application Tracing | Trace context, model outputs, and tools | Yes | Yes |
| Token & Cost Tracking | Visibility into key metrics | Yes |  No |
| 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 metrics for evaluation | Yes | Yes |
| Span-level Evaluation | Evaluate individual steps taken by an agent | Yes | No |
| 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 | No |
| Evaluation/ Experiment Dashboard | Interface to monitor evaluation results | Yes | Partial |
| Agent Evaluation | Evaluate complex AI apps and agentic systems | Yes | Partial |
| Evaluation and Human Feedback for Conversations | Track annotator insights & scores in production | Yes | No |
| 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 | Partial |
| Prompt Playground | Test & refine prompts and outputs from LLMs | Yes | Yes |
| Agent Optimization | |||
| Automated Agent Optimization | Automatically refine entire agents & prompts | Yes | Partial |
| 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 purpose-built for teams developing LLM-powered and agentic applications, with a focus on understanding, evaluating, and improving complex AI behavior in production.
Deep Agent Evaluation
Opik supports trace-level, step-level, and thread-level evaluation, enabling scoring of full agent executions rather than isolated prompt responses.
Automated Optimization Workflows
Opik can automatically optimize prompts, tool definitions, and agent parameters, reducing reliance on manual trial-and-error.
Production-grade GenAI Observability
Opik provides native tracing, cost tracking, online evaluation, dashboards, and alerts tailored to LLM applications.
MLflow GenAI’s Advantages
MLFlow GenAI offers a flexible interface where GenAI functionality can be combined into existing ML experimentation and tracking workflows
GenAI Support
MLflow allows teams to add prompt tracking and evaluation capabilities via SDK-based extensions and custom logic.
Single System for Experimentation
Ability to manage GenAI experiments alongside other ML experimentation workflows within the same interface.
Broad Adoption and Ecosystem Maturity
MLflow is broadly adopted across engineering teams, making it easy to integrate into established workflows and internal tooling.
“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
Ready to Upgrade Your AI Development Workflows?
Join the growing number of developers who’ve turned to Opik for superior performance, flexibility, and advanced features when building AI applications.