Decision comparison
Comet ML vs Weights & Biases
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 month for up to 50 team members. Weights & Biases differentiates with its prominent experiment visualization, dedicated hyperparameter Sweeps feature, extensive framework integrations across 40+ AI tools, and a sizable, 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.
Direct comparison. These are reviewed substitutes bought for the same job, so the differences below are the ones that decide between them.
All 2 are experiment tracking.
Quick Comparison
| Decision factor | 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 Comet MLOps (experiment management) has a Free plan for 1 platform user, Pro at $19 per user per month for up to 10 users, and custom-priced Enterprise. Opik Pro Cloud is $19 per month for up to 50 team members. Verified 2026-10-05 against https://www.comet.com/site/pricing/. | Weights & Biases is now part of CoreWeave Forge, whose pricing page lists the plans. Free: $0 per month, up to 5 model seats, 5 GB of storage and 1 GB of Weave data ingestion a month. Pro: starts at $60 per month, billed monthly, for early-stage teams with fewer than 50 employees; up to 10 model seats, 100 GB of storage and 1.5 GB of Weave data ingestion a month; additional storage $0.03 per GB and additional Weave data ingestion $0.10 per MB. Enterprise: custom plans with customizable seats, storage and ingestion; additional Weave data ingestion $0.20 per MB. Academic: free, with unlimited projects and teams and 200 GB of cloud storage. Verified 2026-10-09 against https://coreweave.com/forge-pricing. |
| 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 |
Comet ML
- Primary Focus:
- End-to-end model evaluation spanning LLM observability, experiment tracking, and production monitoring
- GenAI Capabilities:
- Opik platform with LLM tracing, automated prompt engineering, evaluation metrics, and agent optimization
- Experiment Tracking:
- Full experiment management with code versioning, custom dashboards, and interactive visualizations
- Open Source:
- Opik is fully open source with 18,000+ GitHub stars; same codebase for self-hosted and cloud versions
- Pricing Model:
- Free tier $0, Pro $19/mo, Enterprise custom Comet MLOps (experiment management) has a Free plan for 1 platform user, Pro at $19 per user per month for up to 10 users, and custom-priced Enterprise. Opik Pro Cloud is $19 per month for up to 50 team members. Verified 2026-10-05 against https://www.comet.com/site/pricing/.
- Best For:
- Teams needing both classical ML experiment tracking and GenAI evaluation in a single platform
Weights & Biases
- Primary Focus:
- ML experiment tracking and model management with deep visualization and collaboration tools
- GenAI Capabilities:
- AI application evaluations, tracing, and scorers through the Weave platform
- Experiment Tracking:
- Industry-leading run comparison with rich charts, tables, and team collaboration features
- Open Source:
- Core SDK is open source with MIT license and 11,000+ GitHub stars; server is proprietary
- Pricing Model:
- Weights & Biases is now part of CoreWeave Forge, whose pricing page lists the plans. Free: $0 per month, up to 5 model seats, 5 GB of storage and 1 GB of Weave data ingestion a month. Pro: starts at $60 per month, billed monthly, for early-stage teams with fewer than 50 employees; up to 10 model seats, 100 GB of storage and 1.5 GB of Weave data ingestion a month; additional storage $0.03 per GB and additional Weave data ingestion $0.10 per MB. Enterprise: custom plans with customizable seats, storage and ingestion; additional Weave data ingestion $0.20 per MB. Academic: free, with unlimited projects and teams and 200 GB of cloud storage. Verified 2026-10-09 against https://coreweave.com/forge-pricing.
- Best For:
- Research teams and ML engineers who prioritize experiment visualization, sweeps, and team collaboration
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Comet ML | Weights & Biases |
|---|---|---|
| Search interest(Market interest) | 0 | 0 |
| Product Hunt comments(Community interest) | 14 | 8 |
| Product Hunt rating(Community interest) | Unavailable | 5.0/5 |
| Product Hunt reviews(Community interest) | 0 | 3 |
| Product Hunt votes(Community interest) | 199 | 110 |
| PyPI weekly downloads(Developer adoption) | 77.9k | Not available |
| Stack Overflow questions(Community interest) | 11 | 139 |
| Docker Hub pulls(Product adoption) | Not available | 4.1M |
| GitHub commits, 90d(Product adoption) | Not available | 521 |
| GitHub stars(Product adoption) | Not available | 11,000+ |
| Hacker News mentions, 90d(Community interest) | Not available | 0 |
| Hugging Face downloads(Product adoption) | Not available | 3.4k |
| Hugging Face likes(Product adoption) | Not available | 32 |
| npm weekly downloads(Developer adoption) | Not available | 11.8k |
| PyPI weekly downloads(Product adoption) | Not available | 3.8M |
As of October 5, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Comet ML
October 5, 2026Package vulnerabilities
PyPI · comet-ml@3.58.7
0 vulnerabilities
across 1 package
Repository security score
Not available
Weights & Biases
October 5, 2026Package vulnerabilities
npm · @wandb/sdk@0.5.1 · PyPI · wandb@0.30.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
Interface Preview
Comet ML
Feature Comparison
| 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 |
Experiment Tracking & Visualization
Run Logging & Comparison
Custom Dashboards
Hyperparameter Optimization
GenAI & LLM Capabilities
LLM Tracing
Evaluation Metrics
Prompt Optimization
Model Management & Registry
Model Versioning
Production Monitoring
Dataset Management
Collaboration & Access Control
Team Collaboration
Access Controls
Integrations
Deployment & Infrastructure
Self-Hosting
Cloud Options
Compliance
Which to choose
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 month for up to 50 team members. Weights & Biases differentiates with its prominent experiment visualization, dedicated hyperparameter Sweeps feature, extensive framework integrations across 40+ AI tools, and a sizable, 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.
Best-fit scenarios
Choose Comet ML if:
Choose Comet ML if your team needs a unified platform that covers both traditional ML experiment tracking and GenAI observability without switching tools. Opik's open-source LLM tracing, built-in evaluation metrics, and automated prompt engineering give Comet a clear advantage for teams building and optimizing LLM-powered applications and multi-step agents. The Pro plan at $19 per month for up to 50 team members makes Comet a lower-cost entry point for growing teams that need collaboration features without enterprise-tier pricing. Organizations that value true open-source deployments for their LLM evaluation infrastructure will also benefit from Opik's self-hosted option.
Choose Weights & Biases if:
Choose Weights & Biases if your team's primary workflow centers on experiment tracking, model training, and hyperparameter optimization. W&B's visualization tools, Sweeps for automated hyperparameter search, and collaborative reporting are the most mature in the MLOps space. The platform integrates with over 40 AI frameworks and model providers, making it the more versatile choice for teams working across diverse ML stacks including PyTorch, JAX, TensorFlow, and Hugging Face. Research teams and organizations with established W&B workflows will find the extensive experiment comparison features and sizable community ecosystem difficult to replace.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
What is the main difference between Comet ML and Weights & Biases?
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.
How do Comet ML and Weights & Biases compare on pricing?
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 month for 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/month, billed monthly with up to 10 model seats and 100 GB of storage. Both platforms offer custom Enterprise pricing. Because the vendors publish different pricing bases, compare current plan terms and expected usage instead of multiplying W&B's starting price by seats.
Which platform has better open-source support?
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.
Which tool is better for GenAI and LLM development?
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 extensive framework integration ecosystem and mature experiment tracking make it a strong choice for teams that split time between traditional ML training and GenAI application development.