Comet ML and Weights & Biases are the two most established MLOps platforms for experiment tracking, and both have expanded aggressively into GenAI observability and evaluation. Comet ML differentiates with its fully open-source Opik platform for LLM tracing and evaluation, automated prompt engineering capabilities, and a lower Pro-tier price point at $19 per user per month. Weights & Biases differentiates with its industry-leading experiment visualization, dedicated hyperparameter Sweeps feature, broader framework integrations across 40+ AI tools, and a large, active open-source community around its Python SDK. Both platforms offer free tiers, enterprise-grade security, and self-hosting options. The right choice depends on whether your team prioritizes GenAI evaluation tooling and cost efficiency or experiment visualization depth and integration breadth.
| Feature | Comet ML | Weights & Biases |
|---|---|---|
| Primary Focus | End-to-end model evaluation spanning LLM observability, experiment tracking, and production monitoring | ML experiment tracking and model management with deep visualization and collaboration tools |
| GenAI Capabilities | Opik platform with LLM tracing, automated prompt engineering, evaluation metrics, and agent optimization | AI application evaluations, tracing, and scorers through the Weave platform |
| Experiment Tracking | Full experiment management with code versioning, custom dashboards, and interactive visualizations | Industry-leading run comparison with rich charts, tables, and team collaboration features |
| Open Source | Opik is fully open source with 18,000+ GitHub stars; same codebase for self-hosted and cloud versions | Core SDK is open source with MIT license and 11,000+ GitHub stars; server is proprietary |
| Pricing Model | Free tier $0, Pro $19/mo, Enterprise custom | Free (Free tier), $60/mo (Pro), CONTACT US (Enterprise) |
| Best For | Teams needing both classical ML experiment tracking and GenAI evaluation in a single platform | Research teams and ML engineers who prioritize experiment visualization, sweeps, and team collaboration |
| Metric | Comet ML | Weights & Biases |
|---|---|---|
| GitHub stars | — | 11.0k |
| TrustRadius rating | 8.0/10 (1 reviews) | 10.0/10 (2 reviews) |
| PyPI weekly downloads | 167.7k | 5.6M |
| Search interest | 0 | 0 |
| Product Hunt votes | 189 | — |
As of 2026-05-04 — updated weekly.
| Feature | Comet ML | Weights & Biases |
|---|---|---|
| Experiment Tracking & Visualization | ||
| Run Logging & Comparison | Automatic logging of metrics, hyperparameters, code, and git commits with side-by-side comparison | Rich run logging with interactive charts, tables, and parallel coordinate plots for deep comparison |
| Custom Dashboards | Custom panels and interactive visualizations for tracking metrics across experiments | Flexible workspace with drag-and-drop panels, custom charts, and shareable reports |
| Hyperparameter Optimization | Parameter optimization through experiment comparison and built-in search tools | Dedicated Sweeps feature with Bayesian, grid, and random search strategies across distributed runs |
| GenAI & LLM Capabilities | ||
| LLM Tracing | Opik provides full LLM observability with trace visualization, session tracking, and error surfacing | Weave platform offers AI application tracing with evaluation scorers and pipeline visibility |
| Evaluation Metrics | Built-in LLM-as-a-judge metrics for hallucination, context precision, relevance, and factuality | AI application scorers for evaluating model outputs across custom and predefined criteria |
| Prompt Optimization | Automated prompt engineering that generates and tests prompts for agentic system steps | Manual prompt iteration through experiment tracking and comparison workflows |
| Model Management & Registry | ||
| Model Versioning | Model registry with version tracking, dataset management, and full reproducibility | Artifact registry with lineage tracking, model versioning, and automated CI/CD triggers |
| Production Monitoring | Dedicated production monitoring for data drift detection and model performance degradation | Production monitoring through logged metrics and alerting via Slack and email integrations |
| Dataset Management | Dataset versioning and management as a core platform capability alongside experiment tracking | Data versioning through the Artifacts system with storage tracking and lineage graphs |
| Collaboration & Access Control | ||
| Team Collaboration | Team workspaces with up to 10 members on free tier and 50 on Pro for shared experiment review | Unlimited teams on Pro with collaborative workspaces, shared reports, and team dashboards |
| Access Controls | Enterprise-only SSO, RBAC, and compliance certifications including SOC 2, ISO 27001, and HIPAA | Team-based access controls on Pro; SSO, custom roles, and audit logs on Enterprise |
| Integrations | Integrations with PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, Hugging Face, and LlamaIndex | Integrations with PyTorch, TensorFlow, Keras, JAX, Hugging Face, LangChain, and 40+ AI frameworks |
| Deployment & Infrastructure | ||
| Self-Hosting | Opik can be self-hosted as true OSS; Comet MLOps supports self-hosted and on-premise deployments | Self-hosted server available via Docker for personal use; enterprise self-hosting with dedicated infrastructure |
| Cloud Options | Managed cloud with flexible deployment options and enterprise-grade security backed by Comet infrastructure | Managed cloud with single-tenant option, choice of region, and secure private connectivity on Enterprise |
| Compliance | SOC 2, ISO 27001, ISO 9001, HIPAA, and GDPR compliance on Enterprise tier | HIPAA compliant option with customer-managed encryption keys on Enterprise tier |
Run Logging & Comparison
Custom Dashboards
Hyperparameter Optimization
LLM Tracing
Evaluation Metrics
Prompt Optimization
Model Versioning
Production Monitoring
Dataset Management
Team Collaboration
Access Controls
Integrations
Self-Hosting
Cloud Options
Compliance
Comet ML and Weights & Biases are the two most established MLOps platforms for experiment tracking, and both have expanded aggressively into GenAI observability and evaluation. Comet ML differentiates with its fully open-source Opik platform for LLM tracing and evaluation, automated prompt engineering capabilities, and a lower Pro-tier price point at $19 per user per month. Weights & Biases differentiates with its industry-leading experiment visualization, dedicated hyperparameter Sweeps feature, broader framework integrations across 40+ AI tools, and a large, active open-source community around its Python SDK. Both platforms offer free tiers, enterprise-grade security, and self-hosting options. The right choice depends on whether your team prioritizes GenAI evaluation tooling and cost efficiency or experiment visualization depth and integration breadth.
Choose Comet ML if:
Choose Weights & Biases if:
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Comet ML positions itself as an end-to-end model evaluation platform that spans both traditional MLOps and GenAI observability. Its Opik product provides open-source LLM tracing, evaluation, and automated prompt engineering, while Comet MLOps handles experiment tracking, model versioning, and production monitoring. Weights & Biases focuses primarily on experiment tracking and model management with industry-leading visualization and collaboration tools. W&B has expanded into GenAI with its Weave platform for AI application evaluations and tracing. The core distinction is that Comet emphasizes breadth across the evaluation lifecycle, while W&B emphasizes depth in experiment tracking and visualization.
Comet ML offers a lower entry price for paid plans. The free cloud tier supports up to 10 team members with 25,000 spans per month, and the Pro plan costs $19 per user per month with up to 50 team members and 100,000 spans. Weights & Biases provides a free tier with 5 seats and 5 GB of storage per month. The Pro plan starts at $60 per user per month with up to 10 model seats and 100 GB of storage. Both platforms offer custom Enterprise pricing. For teams scaling beyond the free tier, Comet ML is roughly three times less expensive per user on the Pro plan, though storage and usage overages may affect the total cost for both platforms.
Comet ML has a stronger open-source story through Opik, its LLM observability and evaluation platform. Opik is fully open source with over 18,000 GitHub stars and runs the same codebase for both self-hosted and cloud-hosted versions. Teams can download, install, and run Opik on their own infrastructure with the complete feature set. Weights & Biases has an open-source Python SDK under the MIT license with over 11,000 GitHub stars, but the server component is proprietary. W&B offers a self-hosted server option via Docker for personal projects, but enterprise self-hosting requires a license. For teams that need full control over their deployment, Comet's Opik offers more flexibility.
Both platforms have invested heavily in GenAI capabilities. Comet ML's Opik platform provides LLM tracing with agent execution graphs, session tracking, built-in evaluation metrics for hallucination and relevance, and an automated prompt optimization suite that generates and tests prompts for agentic systems. Weights & Biases offers Weave for AI application evaluations, tracing, and scorers, with integrations across 40+ AI frameworks and model providers. Comet's automated prompt engineering and agent optimization features give it an edge for teams building complex multi-step agents. W&B's broader framework integration ecosystem and mature experiment tracking make it stronger for teams that split time between traditional ML training and GenAI application development.