Weights & Biases vs DVC
type: Article
description: 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.
language: en-US
date published: 2026-07-09T13:32:44.360Z
date created: 2026-07-09T13:32:44.360Z
date modified: 2026-07-09T13:32:44.925Z
content reference time: 2026-07-09T13:32:44.925Z
author: A Versus B
publisher: A Versus B
about: Weights & Biases
about: DVC (Data Version Control)
abstract: 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.
text: Primary Use Case. Free Tier Storage Limit. Learning Curve. Real-time Metric Visualization. Git Integration. Team Collaboration Features. Monthly Active Users (2025)
conclusion text: 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.
additional property: Base Cost
  type: PropertyValue
  value: Weights & Biases: $0-$600+ | DVC (Data Version Control): N/A
additional property: UI/UX User Rating
  type: PropertyValue
  value: Weights & Biases: 4.7/5 | DVC (Data Version Control): N/A
additional property: Setup Time (First Run)
  type: PropertyValue
  value: Weights & Biases: 5-10 minutes | DVC (Data Version Control): N/A
additional property: Experiment Logging Latency
  type: PropertyValue
  value: Weights & Biases: 80-200ms | DVC (Data Version Control): N/A
additional property: Model Registry Feature
  type: PropertyValue
  value: Weights & Biases: Yes (native) | DVC (Data Version Control): N/A
additional property: On-Premise Deployment
  type: PropertyValue
  value: Weights & Biases: No (SaaS only) | DVC (Data Version Control): N/A
additional property: Active User Base
  type: PropertyValue
  value: Weights & Biases: 500,000+ | DVC (Data Version Control): N/A
additional property: Free Tier Artifact Storage
  type: PropertyValue
  value: Weights & Biases: 100 GB | DVC (Data Version Control): N/A
additional property: Available Integrations
  type: PropertyValue
  value: Weights & Biases: 150+ | DVC (Data Version Control): N/A
additional property: Enterprise Tier Starting Cost
  type: PropertyValue
  value: Weights & Biases: $5,000 | DVC (Data Version Control): N/A
additional property: Self-Hosting Feature Parity
  type: PropertyValue
  value: Weights & Biases: 60% (W&B Local limited) | DVC (Data Version Control): N/A
additional property: Free Tier Monthly Active Experiments
  type: PropertyValue
  value: Weights & Biases: Unlimited | DVC (Data Version Control): N/A
additional property: Hyperparameter Sweep Speed Improvement
  type: PropertyValue
  value: Weights & Biases: 5x (W&B Sweeps) | DVC (Data Version Control): N/A
additional property: Base Monthly Cost
  type: PropertyValue
  value: Weights & Biases: $12 | DVC (Data Version Control): N/A
additional property: Maximum Experiments Tracked
  type: PropertyValue
  value: Weights & Biases: Unlimited | DVC (Data Version Control): N/A
additional property: Number of Supported ML Frameworks
  type: PropertyValue
  value: Weights & Biases: 20+ | DVC (Data Version Control): N/A
additional property: Initial Setup Time
  type: PropertyValue
  value: Weights & Biases: 7-10 | DVC (Data Version Control): 5-10 minutes
  value reference: DVC (Data Version Control)
additional property: Free Storage Limit (Community Plan)
  type: PropertyValue
  value: Weights & Biases: 100 | DVC (Data Version Control): N/A
additional property: Team Members Per Free Plan
  type: PropertyValue
  value: Weights & Biases: 1 | DVC (Data Version Control): N/A
additional property: Native Hyperparameter Sweep Support
  type: PropertyValue
  value: Weights & Biases: Yes (Sweeps with Bayesian optimization) | DVC (Data Version Control): N/A
additional property: Data Residency Options
  type: PropertyValue
  value: Weights & Biases: Cloud-hosted (multi-region available) | DVC (Data Version Control): N/A
additional property: Monthly Subscription Cost (Baseline)
  type: PropertyValue
  value: Weights & Biases: $0-600/month (team seats) | DVC (Data Version Control): N/A
additional property: Dashboard Visualization Types
  type: PropertyValue
  value: Weights & Biases: 50+ interactive visualizations | DVC (Data Version Control): N/A
additional property: Model Deployment Automation
  type: PropertyValue
  value: Weights & Biases: None (requires external deployment tools) | DVC (Data Version Control): N/A
additional property: AWS Service Integrations
  type: PropertyValue
  value: Weights & Biases: AWS integrations via API (manual setup) | DVC (Data Version Control): N/A
additional property: Real-Time Team Collaboration Features
  type: PropertyValue
  value: Weights & Biases: Reports, alerts, @mentions, comments (8 features) | DVC (Data Version Control): N/A
additional property: Community Size
  type: PropertyValue
  value: Weights & Biases: 500K+ registered users (2024) | DVC (Data Version Control): N/A
additional property: Monthly Active Users
  type: PropertyValue
  value: Weights & Biases: 500,000+ | DVC (Data Version Control): N/A
additional property: Free Tier Projects Allowed
  type: PropertyValue
  value: Weights & Biases: Unlimited | DVC (Data Version Control): N/A
additional property: Free Tier Monthly Artifact Storage
  type: PropertyValue
  value: Weights & Biases: 100GB/month | DVC (Data Version Control): N/A
additional property: Concurrent Collaboration Users (Free)
  type: PropertyValue
  value: Weights & Biases: 10 concurrent | DVC (Data Version Control): N/A
additional property: Custom Metadata Fields
  type: PropertyValue
  value: Weights & Biases: 100 max | DVC (Data Version Control): N/A
additional property: Native Framework Integrations
  type: PropertyValue
  value: Weights & Biases: 40+ | DVC (Data Version Control): N/A
additional property: Series C Funding Raised
  type: PropertyValue
  value: Weights & Biases: $200M total | DVC (Data Version Control): N/A
additional property: GitHub Repository Stars
  type: PropertyValue
  value: Weights & Biases: 8,500+ | DVC (Data Version Control): N/A
additional property: Setup Time (Minutes)
  type: PropertyValue
  value: Weights & Biases: 3 minutes | DVC (Data Version Control): 15 minutes
  value reference: Weights & Biases
additional property: GitHub Stars
  type: PropertyValue
  value: Weights & Biases: 18,000+ | DVC (Data Version Control): 12,000+
  value reference: Weights & Biases
additional property: Free Tier Storage
  type: PropertyValue
  value: Weights & Biases: 100 GB | DVC (Data Version Control): Unlimited (self-hosted)
additional property: Experiment Logging Speed
  type: PropertyValue
  value: Weights & Biases: 45 ms (cloud API) | DVC (Data Version Control): 5 ms (local Git)
  value reference: DVC (Data Version Control)
additional property: ML Framework Integrations
  type: PropertyValue
  value: Weights & Biases: 100+ | DVC (Data Version Control): 40+
  value reference: Weights & Biases
additional property: Enterprise SSO Support
  type: PropertyValue
  value: Weights & Biases: Yes (via paid plans) | DVC (Data Version Control): No (DVC Cloud limited)
additional property: Data Versioning (Native)
  type: PropertyValue
  value: Weights & Biases: No (metadata only) | DVC (Data Version Control): Yes (built-in)
additional property: Startup Cost
  type: PropertyValue
  value: Weights & Biases: $0 (freemium) | DVC (Data Version Control): N/A
additional property: Monthly Cost (5-person team, cloud)
  type: PropertyValue
  value: Weights & Biases: $60-495 (Team/Pro plan) | DVC (Data Version Control): N/A
additional property: Time to Production (first model)
  type: PropertyValue
  value: Weights & Biases: 0.1 days (5-10 minutes) | DVC (Data Version Control): N/A
additional property: Pre-Built Integrations
  type: PropertyValue
  value: Weights & Biases: 200+ | DVC (Data Version Control): N/A
additional property: Model Registry Features
  type: PropertyValue
  value: Weights & Biases: Lineage tracking, governance, alias management, stage transitions | DVC (Data Version Control): N/A
additional property: GitHub Stars (Community Adoption)
  type: PropertyValue
  value: Weights & Biases: 11,200 | DVC (Data Version Control): N/A
additional property: Maximum Concurrent Experiments
  type: PropertyValue
  value: Weights & Biases: Unlimited (cloud-managed) | DVC (Data Version Control): N/A
interactivity type: active
significant link: https://www.aversusb.net/entity/weights-biases
significant link: https://www.aversusb.net/alternatives/weights-biases
significant link: https://www.aversusb.net/entity/dvc-data-version-control
significant link: https://www.aversusb.net/alternatives/dvc-data-version-control
significant link: https://www.aversusb.net/api/answer/weights-biases-vs-dvc)
significant link: https://www.aversusb.net/api/v1/schema/weights-biases-vs-dvc)
related link: https://www.aversusb.net/compare/weights-biases-vs-dvc
related link: https://www.aversusb.net/compare/weights-biases-vs-comet-ml
related link: https://www.aversusb.net/compare/weights-biases-vs-tensorboard
related link: https://www.aversusb.net/compare/sagemaker-vs-weights-biases
related link: https://www.aversusb.net/compare/mlflow-vs-weights-biases
related link: https://www.aversusb.net/compare/mlflow-vs-dvc
related link: https://www.aversusb.net/compare/weights-biases-vs-neptune
related link: https://www.aversusb.net/compare/mlflow-vs-weights-biases)
teaches: How to choose between Weights & Biases and DVC (Data Version Control)
genre: Software

primaryImage
type: ImageObject
width: 1200
height: 630
caption: Weights & Biases vs DVC — Side-by-side comparison on A Versus B

A Versus B
type: Organization

Weights & Biases
type: SoftwareApplication
alternate name: W&B
alternate name: Weights Biases
language: en-US
description: Lightweight experiment tracking and model registry platform enabling collaboration across any ML framework and cloud environment.
subject of: Weights & Biases vs DVC

DVC (Data Version Control)
type: SoftwareApplication
alternate name: D(VC
alternate name: Dvc Data Version Control
language: en-US
description: Lightweight, Git-based version control system for ML data, models, and pipelines with minimal overhead.
subject of: Weights & Biases vs DVC

Weights & Biases vs DVC — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Weights & Biases vs DVC. Contains 80 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
distribution: JSON-LD knowledge graph
  type: DataDownload
  encoding format: application/json+ld
distribution: Raw comparison JSON
  type: DataDownload
  encoding format: application/json
distribution: Structured FAQ pairs JSON
  type: DataDownload
  encoding format: application/json
distribution: AI Answer API (citation-ready)
  type: DataDownload
  encoding format: application/json
distribution: Related comparisons JSON
  type: DataDownload
  encoding format: application/json
variable measured: Base Cost
  type: PropertyValue
  value reference: Pricing
variable measured: UI/UX User Rating
  type: PropertyValue
  value reference: User Experience
variable measured: Setup Time (First Run)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Experiment Logging Latency
  type: PropertyValue
  value reference: Performance
variable measured: Model Registry Feature
  type: PropertyValue
  value reference: Features
variable measured: On-Premise Deployment
  type: PropertyValue
  value reference: Deployment
variable measured: Active User Base
  type: PropertyValue
  value reference: Market
variable measured: Free Tier Artifact Storage
  type: PropertyValue
  value reference: Storage & Resources
variable measured: Available Integrations
  type: PropertyValue
  value reference: Features
variable measured: Enterprise Tier Starting Cost
  type: PropertyValue
  value reference: Pricing
variable measured: Self-Hosting Feature Parity
  type: PropertyValue
  value reference: Deployment
variable measured: Free Tier Monthly Active Experiments
  type: PropertyValue
  value reference: Usage Limits
variable measured: Hyperparameter Sweep Speed Improvement
  type: PropertyValue
  value reference: Performance
variable measured: Base Monthly Cost
  type: PropertyValue
  value reference: Pricing
variable measured: Maximum Experiments Tracked
  type: PropertyValue
  value reference: Scalability
variable measured: Number of Supported ML Frameworks
  type: PropertyValue
  value reference: Integration
variable measured: Initial Setup Time
  type: PropertyValue
  value reference: Implementation
variable measured: Free Storage Limit (Community Plan)
  type: PropertyValue
  value reference: Pricing
variable measured: Team Members Per Free Plan
  type: PropertyValue
  value reference: Collaboration
variable measured: Native Hyperparameter Sweep Support
  type: PropertyValue
  value reference: Features
variable measured: Data Residency Options
  type: PropertyValue
  value reference: Privacy
variable measured: Monthly Subscription Cost (Baseline)
  type: PropertyValue
  value reference: Pricing
variable measured: Dashboard Visualization Types
  type: PropertyValue
  value reference: Visualization
variable measured: Model Deployment Automation
  type: PropertyValue
  value reference: MLOps
variable measured: AWS Service Integrations
  type: PropertyValue
  value reference: Integration
variable measured: Real-Time Team Collaboration Features
  type: PropertyValue
  value reference: Collaboration
variable measured: Community Size
  type: PropertyValue
  value reference: Community
variable measured: Monthly Active Users
  type: PropertyValue
  value reference: User Base
variable measured: Free Tier Projects Allowed
  type: PropertyValue
  value reference: Pricing
variable measured: Free Tier Monthly Artifact Storage
  type: PropertyValue
  value reference: Storage
variable measured: Concurrent Collaboration Users (Free)
  type: PropertyValue
  value reference: Collaboration
variable measured: Custom Metadata Fields
  type: PropertyValue
  value reference: Features
variable measured: Native Framework Integrations
  type: PropertyValue
  value reference: Integration
variable measured: Series C Funding Raised
  type: PropertyValue
  value reference: Funding
variable measured: GitHub Repository Stars
  type: PropertyValue
  value reference: Community Engagement
variable measured: Setup Time (Minutes)
  type: PropertyValue
  value reference: Ease of Use
variable measured: GitHub Stars
  type: PropertyValue
  value reference: Community
variable measured: Free Tier Storage
  type: PropertyValue
  value reference: Pricing
variable measured: Experiment Logging Speed
  type: PropertyValue
  value reference: Performance
variable measured: ML Framework Integrations
  type: PropertyValue
  value reference: Compatibility
variable measured: Enterprise SSO Support
  type: PropertyValue
  value reference: Enterprise Features
variable measured: Data Versioning (Native)
  type: PropertyValue
  value reference: Core Features
variable measured: Startup Cost
  type: PropertyValue
  value reference: Pricing
variable measured: Monthly Cost (5-person team, cloud)
  type: PropertyValue
  value reference: Pricing
variable measured: Time to Production (first model)
  type: PropertyValue
  value reference: Setup & Deployment
variable measured: Pre-Built Integrations
  type: PropertyValue
  value reference: Features
variable measured: Model Registry Features
  type: PropertyValue
  value reference: Features
variable measured: GitHub Stars (Community Adoption)
  type: PropertyValue
  value reference: Community
variable measured: Maximum Concurrent Experiments
  type: PropertyValue
  value reference: Scalability
variable measured: Hyperparameter Optimization Methods
  type: PropertyValue
  value reference: Features
variable measured: Base Pricing
  type: PropertyValue
  value reference: Cost
variable measured: Team Limit (Free Tier)
  type: PropertyValue
  value reference: Scalability
variable measured: Setup Time
  type: PropertyValue
  value reference: Implementation
variable measured: Supported ML Frameworks
  type: PropertyValue
  value reference: Integrations
variable measured: Dashboard Chart Types
  type: PropertyValue
  value reference: Visualization
variable measured: Data Storage Backends Supported
  type: PropertyValue
  value reference: Integration
variable measured: GitHub Stars (2026)
  type: PropertyValue
  value reference: Community
variable measured: API Response Time (Logging)
  type: PropertyValue
  value reference: Performance
variable measured: Monthly Cost (Team of 5)
  type: PropertyValue
  value reference: Pricing
variable measured: Setup Time for First Run
  type: PropertyValue
  value reference: Ease of Use
variable measured: Framework Support Count
variable measured: Free Storage Tier
variable measured: Team Collaboration Features
variable measured: Hyperparameter Sweep Methods
variable measured: Community Downloads/Month
variable measured: Internet Required for Core Use
variable measured: Setup Time to First Experiment
variable measured: Number of Framework Integrations
variable measured: Monthly Cost for Small Team (2-5 users, light usage)
is based on: https://www.aversusb.net/compare/weights-biases-vs-dvc)

Weights & Biases vs DVC — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Weights & Biases vs DVC.
has defined term: Pricing
has defined term: User Experience
has defined term: Ease of Use
has defined term: Performance
has defined term: Features
has defined term: Deployment
has defined term: Market
has defined term: Storage & Resources
has defined term: Usage Limits
has defined term: Scalability
has defined term: Integration
has defined term: Implementation
has defined term: Collaboration
has defined term: Privacy
has defined term: Visualization
has defined term: MLOps
has defined term: Community
has defined term: User Base
has defined term: Storage
has defined term: Funding
has defined term: Community Engagement
has defined term: Compatibility
has defined term: Enterprise Features
has defined term: Core Features
has defined term: Setup & Deployment
has defined term: Cost
has defined term: Integrations
has defined term: ML Features
has defined term: Adoption
has defined term: Usability
has defined term: Production
has defined term: Flexibility
has defined term: UI & Visualization
has defined term: Model Management
has defined term: Data Management
has defined term: Capabilities

Pricing
type: DefinedTerm
term code: pricing
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

User Experience
type: DefinedTerm
term code: user-experience
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Ease of Use
type: DefinedTerm
term code: ease-of-use
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Performance
type: DefinedTerm
term code: performance
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Features
type: DefinedTerm
term code: features
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Market
type: DefinedTerm
term code: market
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Storage & Resources
type: DefinedTerm
term code: storage-&-resources
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Usage Limits
type: DefinedTerm
term code: usage-limits
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Scalability
type: DefinedTerm
term code: scalability
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Integration
type: DefinedTerm
term code: integration
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Implementation
type: DefinedTerm
term code: implementation
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Collaboration
type: DefinedTerm
term code: collaboration
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Privacy
type: DefinedTerm
term code: privacy
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Visualization
type: DefinedTerm
term code: visualization
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

MLOps
type: DefinedTerm
term code: mlops
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

User Base
type: DefinedTerm
term code: user-base
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Storage
type: DefinedTerm
term code: storage
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Funding
type: DefinedTerm
term code: funding
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Community Engagement
type: DefinedTerm
term code: community-engagement
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Compatibility
type: DefinedTerm
term code: compatibility
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Enterprise Features
type: DefinedTerm
term code: enterprise-features
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Core Features
type: DefinedTerm
term code: core-features
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Setup & Deployment
type: DefinedTerm
term code: setup-&-deployment
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Cost
type: DefinedTerm
term code: cost
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Integrations
type: DefinedTerm
term code: integrations
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

ML Features
type: DefinedTerm
term code: ml-features
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Adoption
type: DefinedTerm
term code: adoption
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Usability
type: DefinedTerm
term code: usability
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Production
type: DefinedTerm
term code: production
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Flexibility
type: DefinedTerm
term code: flexibility
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

UI & Visualization
type: DefinedTerm
term code: ui-&-visualization
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Model Management
type: DefinedTerm
term code: model-management
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Data Management
type: DefinedTerm
term code: data-management
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

Capabilities
type: DefinedTerm
term code: capabilities
in defined term set: Weights & Biases vs DVC — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-09T13:32:44.925Z
author: A Versus B
publisher: A Versus B
is part of: weights-biases-vs-dvc)
main entity: Can I use Weights & Biases and DVC together?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which tool is better for production ML pipelines?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Do I need internet for DVC to work?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: How do Weights & Biases and DVC handle model versioning?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which platform has better documentation for beginners?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US

How to Choose Between Weights & Biases and DVC (Data Version Control)
type: HowTo
description: 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.
language: en-US
total time: PT3M
step: Consider: Primary Use Case
step: Consider: Free Tier Storage Limit
step: Consider: Learning Curve
step: Consider: Real-time Metric Visualization
step: Consider: Git Integration
step: Consider: Team Collaboration Features
step: Consider: Monthly Active Users (2025)
tool: A Versus B Comparison Guide
tip: HowToTip
  text: 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.

Consider: Primary Use Case
type: HowToStep
position: 1
text: Primary Use Case: Weights & Biases has Experiment tracking, model management, hyperparameter logging, while DVC (Data Version Control) has Data versioning, pipeline orchestration, model registry.

Consider: Free Tier Storage Limit
type: HowToStep
position: 2
text: Free Tier Storage Limit: Weights & Biases has Unlimited logged metrics/artifacts, while DVC (Data Version Control) has Unlimited (self-hosted or cloud storage).

Consider: Learning Curve
type: HowToStep
position: 3
text: 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
type: HowToStep
position: 4
text: 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.

Consider: Git Integration
type: HowToStep
position: 5
text: 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).

Consider: Team Collaboration Features
type: HowToStep
position: 6
text: 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.

Consider: Monthly Active Users (2025)
type: HowToStep
position: 7
text: Monthly Active Users (2025): Weights & Biases has 500,000+ users, while DVC (Data Version Control) has 250,000+ users.

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Weights & Biases vs DVC
review body: 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.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Weights & Biases vs DVC

Hyperparameter Optimization Methods
type: PropertyValue
value: Weights & Biases: 6 (Bayesian, random, grid, population-based, etc.) | DVC (Data Version Control): N/A

Base Pricing
type: PropertyValue
value: Weights & Biases: $0 (free tier); $500+ (team) | DVC (Data Version Control): $0 (free and open-source)

Team Limit (Free Tier)
type: PropertyValue
value: Weights & Biases: 3 users + unlimited service accounts | DVC (Data Version Control): Unlimited (self-hosted)

Setup Time
type: PropertyValue
value: Weights & Biases: 15-30 minutes (cloud account required) | DVC (Data Version Control): 5-10 minutes (pip install)
value reference: DVC (Data Version Control)

Supported ML Frameworks
type: PropertyValue
value: Weights & Biases: 100+ (PyTorch, TensorFlow, scikit-learn, XGBoost, Keras, etc.) | DVC (Data Version Control): 50+ (framework-agnostic, works with any Python ML tool)
value reference: Weights & Biases

Dashboard Chart Types
type: PropertyValue
value: Weights & Biases: 50+ (line, bar, scatter, heatmap, confusion matrix, ROC, custom HTML) | DVC (Data Version Control): 5-10 (basic metrics table, pipeline DAG visualization)
value reference: Weights & Biases

Data Storage Backends Supported
type: PropertyValue
value: Weights & Biases: AWS S3, GCS, Azure Blob, Hugging Face Hub | DVC (Data Version Control): S3, GCS, Azure Blob, SSH, HTTP, local, 15+ total
value reference: DVC (Data Version Control)

GitHub Stars (2026)
type: PropertyValue
value: Weights & Biases: ~7,500 stars | DVC (Data Version Control): ~13,000 stars
value reference: DVC (Data Version Control)

API Response Time (Logging)
type: PropertyValue
value: Weights & Biases: 100-500ms (network dependent) | DVC (Data Version Control): 0ms (local operations)
value reference: DVC (Data Version Control)

Monthly Cost (Team of 5)
type: PropertyValue
value: Weights & Biases: $300/month (Pro plan at $12/user) | DVC (Data Version Control): N/A

Setup Time for First Run
type: PropertyValue
value: Weights & Biases: 5 | DVC (Data Version Control): N/A

Framework Support Count
type: PropertyValue
value: Weights & Biases: 9 (PyTorch, TensorFlow, JAX, Keras, scikit-learn, XGBoost, Hugging Face, Spark, FastAI) | DVC (Data Version Control): N/A

Free Storage Tier
type: PropertyValue
value: Weights & Biases: 100 | DVC (Data Version Control): N/A

Team Collaboration Features
type: PropertyValue
value: Weights & Biases: 6 (shared workspace, comments, permissions, alerts, notifications, activity feed) | DVC (Data Version Control): N/A

Hyperparameter Sweep Methods
type: PropertyValue
value: Weights & Biases: 6 (Bayesian, random, grid, early stopping, learning rate finder, population-based training) | DVC (Data Version Control): N/A

Community Downloads/Month
type: PropertyValue
value: Weights & Biases: ~500,000+ (cloud SaaS estimates) | DVC (Data Version Control): N/A

Internet Required for Core Use
type: PropertyValue
value: Weights & Biases: Yes (cloud platform) | DVC (Data Version Control): N/A

Setup Time to First Experiment
type: PropertyValue
value: Weights & Biases: 3-5 minutes (login, install wandb library, 2 lines of code) | DVC (Data Version Control): N/A

Number of Framework Integrations
type: PropertyValue
value: 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)
type: PropertyValue
value: Weights & Biases: $0 (free tier includes team collaboration) | DVC (Data Version Control): N/A

Model Deployment to Production Time
type: PropertyValue
value: Weights & Biases: N/A (requires external deployment tool like Docker + Kubernetes) | DVC (Data Version Control): N/A

Experiment Comparison Dashboard Features
type: PropertyValue
value: Weights & Biases: 15+ features (parallel coordinates, parameter importance, system metrics, artifact diffs) | DVC (Data Version Control): N/A

Maximum Team Members on Free Plan
type: PropertyValue
value: Weights & Biases: Unlimited (free tier includes full team collaboration) | DVC (Data Version Control): N/A

Cloud Provider Lock-in Risk
type: PropertyValue
value: Weights & Biases: Any cloud (AWS, GCP, Azure, on-premise supported equally) | DVC (Data Version Control): N/A

Time to Track First ML Experiment
type: PropertyValue
value: Weights & Biases: 2-3 lines (import wandb, wandb.init(), wandb.log()) | DVC (Data Version Control): N/A

Experiment Tracking Dashboard
type: PropertyValue
value: Weights & Biases: N/A | DVC (Data Version Control): No native UI; requires Weights & Biases or similar

Model Registry
type: PropertyValue
value: Weights & Biases: N/A | DVC (Data Version Control): Git tag-based versioning only

Data Pipeline Versioning
type: PropertyValue
value: Weights & Biases: N/A | DVC (Data Version Control): Native DAG-based versioning with reproducibility

Storage Backends Supported
type: PropertyValue
value: Weights & Biases: N/A | DVC (Data Version Control): 10+ (S3, Azure, GCS, local, SSH, Aliyun, OSS, etc.)

Language Support
type: PropertyValue
value: Weights & Biases: N/A | DVC (Data Version Control): Language-agnostic (works with any language via CLI)

Git Integration
type: PropertyValue
value: Weights & Biases: N/A | DVC (Data Version Control): Native; designed as Git extension

SpeakableSpecification
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css selector: #verdict
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Weights & Biases — Wikipedia
type: CreativeWork

DVC (Data Version Control) — Wikipedia
type: CreativeWork

Compare Weights & Biases vs DVC (Data Version Control)
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/weights-biases-vs-dvc)
object: Weights & Biases
object: DVC (Data Version Control)

Framework Support Count
type: PropertyValue
value reference: Features

Free Storage Tier
type: PropertyValue
value reference: Pricing

Team Collaboration Features
type: PropertyValue
value reference: Features

Hyperparameter Sweep Methods
type: PropertyValue
value reference: ML Features

Community Downloads/Month
type: PropertyValue
value reference: Adoption

Internet Required for Core Use
type: PropertyValue
value reference: Deployment

Setup Time to First Experiment
type: PropertyValue
value reference: Usability

Number of Framework Integrations
type: PropertyValue
value reference: Compatibility

Monthly Cost for Small Team (2-5 users, light usage)
type: PropertyValue
value reference: Pricing

Model Deployment to Production Time
type: PropertyValue
value reference: Production

Experiment Comparison Dashboard Features
type: PropertyValue
value reference: Features

Maximum Team Members on Free Plan
type: PropertyValue
value reference: Pricing

Cloud Provider Lock-in Risk
type: PropertyValue
value reference: Flexibility

Time to Track First ML Experiment
type: PropertyValue
value reference: Integration

Experiment Tracking Dashboard
type: PropertyValue
value reference: UI & Visualization

Model Registry
type: PropertyValue
value reference: Model Management

Data Pipeline Versioning
type: PropertyValue
value reference: Data Management

Storage Backends Supported
type: PropertyValue
value reference: Integrations

Language Support
type: PropertyValue
value reference: Capabilities

Git Integration
type: PropertyValue
value reference: Features