Weights & Biases is a comprehensive ML experiment tracking and model management platform with advanced visualization and collaboration features, while DVC is a lightweight version control system for machine learning pipelines and data. W&B excels at experiment tracking; DVC excels at data versioning and pipeline orchestration.
Weights & Biases vs DVC — Side-by-side comparison on A Versus B
Lightweight experiment tracking and model registry platform enabling collaboration across any ML framework and cloud environment.
Lightweight, Git-based version control system for ML data, models, and pipelines with minimal overhead.
Primary Use Case. Free Tier Storage Limit. Learning Curve. Real-time Metric Visualization. Git Integration. Team Collaboration Features. Monthly Active Users (2025)
Choose Weights & Biases if you need production-grade experiment tracking, real-time visualization, and team collaboration with advanced monitoring. Choose DVC if you prioritize data versioning, lightweight pipeline orchestration, and Git-native workflows with minimal setup overhead.
Weights & Biases: $0-$600+ | DVC (Data Version Control): N/A
Weights & Biases: 4.7/5 | DVC (Data Version Control): N/A
Weights & Biases: 5-10 minutes | DVC (Data Version Control): N/A
Weights & Biases: 80-200ms | DVC (Data Version Control): N/A
Weights & Biases: Yes (native) | DVC (Data Version Control): N/A
Weights & Biases: No (SaaS only) | DVC (Data Version Control): N/A
Weights & Biases: 500,000+ | DVC (Data Version Control): N/A
Weights & Biases: 100 GB | DVC (Data Version Control): N/A
Weights & Biases: 150+ | DVC (Data Version Control): N/A
Weights & Biases: $5,000 | DVC (Data Version Control): N/A
Weights & Biases: 60% (W&B Local limited) | DVC (Data Version Control): N/A
Weights & Biases: Unlimited | DVC (Data Version Control): N/A
Weights & Biases: 5x (W&B Sweeps) | DVC (Data Version Control): N/A
Weights & Biases: $12 | DVC (Data Version Control): N/A
Weights & Biases: 20+ | DVC (Data Version Control): N/A
Weights & Biases: 7-10 | DVC (Data Version Control): 5-10 minutes
Weights & Biases: 100 | DVC (Data Version Control): N/A
Weights & Biases: 1 | DVC (Data Version Control): N/A
Weights & Biases: Yes (Sweeps with Bayesian optimization) | DVC (Data Version Control): N/A
Weights & Biases: Cloud-hosted (multi-region available) | DVC (Data Version Control): N/A
Weights & Biases: $0-600/month (team seats) | DVC (Data Version Control): N/A
Weights & Biases: 50+ interactive visualizations | DVC (Data Version Control): N/A
Weights & Biases: None (requires external deployment tools) | DVC (Data Version Control): N/A
Weights & Biases: AWS integrations via API (manual setup) | DVC (Data Version Control): N/A
Weights & Biases: Reports, alerts, @mentions, comments (8 features) | DVC (Data Version Control): N/A
Weights & Biases: 500K+ registered users (2024) | DVC (Data Version Control): N/A
Weights & Biases: 100GB/month | DVC (Data Version Control): N/A
Weights & Biases: 10 concurrent | DVC (Data Version Control): N/A
Weights & Biases: 100 max | DVC (Data Version Control): N/A
Weights & Biases: 40+ | DVC (Data Version Control): N/A
Weights & Biases: $200M total | DVC (Data Version Control): N/A
Weights & Biases: 8,500+ | DVC (Data Version Control): N/A
Weights & Biases: 3 minutes | DVC (Data Version Control): 15 minutes
Weights & Biases: 18,000+ | DVC (Data Version Control): 12,000+
Weights & Biases: 100 GB | DVC (Data Version Control): Unlimited (self-hosted)
Weights & Biases: 45 ms (cloud API) | DVC (Data Version Control): 5 ms (local Git)
Weights & Biases: 100+ | DVC (Data Version Control): 40+
Weights & Biases: Yes (via paid plans) | DVC (Data Version Control): No (DVC Cloud limited)
Weights & Biases: No (metadata only) | DVC (Data Version Control): Yes (built-in)
Weights & Biases: $0 (freemium) | DVC (Data Version Control): N/A
Weights & Biases: $60-495 (Team/Pro plan) | DVC (Data Version Control): N/A
Weights & Biases: 0.1 days (5-10 minutes) | DVC (Data Version Control): N/A
Weights & Biases: 200+ | DVC (Data Version Control): N/A
Weights & Biases: Lineage tracking, governance, alias management, stage transitions | DVC (Data Version Control): N/A
Weights & Biases: 11,200 | DVC (Data Version Control): N/A
Weights & Biases: Unlimited (cloud-managed) | DVC (Data Version Control): N/A
Weights & Biases: 6 (Bayesian, random, grid, population-based, etc.) | DVC (Data Version Control): N/A
Weights & Biases: $0 (free tier); $500+ (team) | DVC (Data Version Control): $0 (free and open-source)
Weights & Biases: 3 users + unlimited service accounts | DVC (Data Version Control): Unlimited (self-hosted)
Weights & Biases: 15-30 minutes (cloud account required) | DVC (Data Version Control): 5-10 minutes (pip install)
Weights & Biases: 100+ (PyTorch, TensorFlow, scikit-learn, XGBoost, Keras, etc.) | DVC (Data Version Control): 50+ (framework-agnostic, works with any Python ML tool)
Weights & Biases: 50+ (line, bar, scatter, heatmap, confusion matrix, ROC, custom HTML) | DVC (Data Version Control): 5-10 (basic metrics table, pipeline DAG visualization)
Weights & Biases: AWS S3, GCS, Azure Blob, Hugging Face Hub | DVC (Data Version Control): S3, GCS, Azure Blob, SSH, HTTP, local, 15+ total
Weights & Biases: ~7,500 stars | DVC (Data Version Control): ~13,000 stars
Weights & Biases: 100-500ms (network dependent) | DVC (Data Version Control): 0ms (local operations)
Weights & Biases: $300/month (Pro plan at $12/user) | DVC (Data Version Control): N/A
Weights & Biases: 5 | DVC (Data Version Control): N/A
Weights & Biases: 9 (PyTorch, TensorFlow, JAX, Keras, scikit-learn, XGBoost, Hugging Face, Spark, FastAI) | DVC (Data Version Control): N/A
Weights & Biases: 6 (shared workspace, comments, permissions, alerts, notifications, activity feed) | DVC (Data Version Control): N/A
Weights & Biases: 6 (Bayesian, random, grid, early stopping, learning rate finder, population-based training) | DVC (Data Version Control): N/A
Weights & Biases: ~500,000+ (cloud SaaS estimates) | DVC (Data Version Control): N/A
Weights & Biases: Yes (cloud platform) | DVC (Data Version Control): N/A
Weights & Biases: 3-5 minutes (login, install wandb library, 2 lines of code) | DVC (Data Version Control): N/A
Weights & Biases: 500+ integrations and automatic logging across frameworks | DVC (Data Version Control): N/A
Monthly Cost for Small Team (2-5 users, light usage)
Weights & Biases: $0 (free tier includes team collaboration) | DVC (Data Version Control): N/A
Weights & Biases: N/A (requires external deployment tool like Docker + Kubernetes) | DVC (Data Version Control): N/A
Experiment Comparison Dashboard Features
Weights & Biases: 15+ features (parallel coordinates, parameter importance, system metrics, artifact diffs) | DVC (Data Version Control): N/A
Weights & Biases: Unlimited (free tier includes full team collaboration) | DVC (Data Version Control): N/A
Weights & Biases: Any cloud (AWS, GCP, Azure, on-premise supported equally) | DVC (Data Version Control): N/A
Weights & Biases: 2-3 lines (import wandb, wandb.init(), wandb.log()) | DVC (Data Version Control): N/A
Weights & Biases: N/A | DVC (Data Version Control): No native UI; requires Weights & Biases or similar
Weights & Biases: N/A | DVC (Data Version Control): Git tag-based versioning only
Weights & Biases: N/A | DVC (Data Version Control): Native DAG-based versioning with reproducibility
Weights & Biases: N/A | DVC (Data Version Control): 10+ (S3, Azure, GCS, local, SSH, Aliyun, OSS, etc.)
Weights & Biases: N/A | DVC (Data Version Control): Language-agnostic (works with any language via CLI)
Weights & Biases: N/A | DVC (Data Version Control): Native; designed as Git extension
How to choose between Weights & Biases and DVC (Data Version Control)
Compare Weights & Biases vs DVC (Data Version Control)
Weights & Biases vs DVC — Structured Comparison Data
Machine-readable comparison dataset for Weights & Biases vs DVC. Contains 80 attribute comparisons, 5 FAQ pairs.
Weights & Biases vs DVC — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Weights & Biases vs DVC.
Can I use Weights & Biases and DVC together?
Yes, absolutely. Many teams use both tools complementarily: DVC for data versioning and pipeline orchestration, and Weights & Biases for experiment tracking and visualization. They integrate well and don't conflict with each other.
Which tool is better for production ML pipelines?
DVC is better for production pipeline orchestration and data versioning due to its lightweight, Git-based approach. Weights & Biases is better for monitoring production model performance and experiments post-deployment. Consider using both: DVC for pipelines, W&B for monitoring.
DVC works entirely offline for local operations (add, commit, pipeline execution). You only need internet when pushing to remote storage or pulling data. Weights & Biases requires internet connection for full functionality, though some offline logging is possible.
How do Weights & Biases and DVC handle model versioning?
Weights & Biases offers native model registry with automatic versioning, lineage, and deployment tracking through the web interface. DVC uses Git tags and branches to version models alongside code, requiring no separate service. W&B is more feature-rich; DVC is more minimal and Git-native.
Which platform has better documentation for beginners?
DVC has slightly better beginner documentation due to its Git-familiar workflow and smaller API surface. Weights & Biases documentation is comprehensive but more complex, with steeper learning curve for advanced features. Both have excellent community support.
How to Choose Between Weights & Biases and DVC (Data Version Control)
Primary Use Case: Weights & Biases has Experiment tracking, model management, hyperparameter logging, while DVC (Data Version Control) has Data versioning, pipeline orchestration, model registry.
Free Tier Storage Limit: Weights & Biases has Unlimited logged metrics/artifacts, while DVC (Data Version Control) has Unlimited (self-hosted or cloud storage).
Learning Curve: Weights & Biases has Moderate - intuitive dashboard but complex API, while DVC (Data Version Control) has Gentle - Git-like commands, familiar to engineers.
Consider: Real-time Metric Visualization
Real-time Metric Visualization: Weights & Biases has Advanced interactive dashboards with 50+ chart types, while DVC (Data Version Control) has Basic metrics tracking, focuses on pipeline stages.
Git Integration: Weights & Biases has Native Git integration for experiment linking, while DVC (Data Version Control) has Works as Git extension (dvc add, dvc push).
Team Collaboration Features: Weights & Biases has Native reporting, alerts, audit logs, permission controls, while DVC (Data Version Control) has Minimal - relies on Git workflows and external tools.
Monthly Active Users (2025): Weights & Biases has 500,000+ users, while DVC (Data Version Control) has 250,000+ users.