Pricing

Build cutting-edge models in half the time

Free teams available for academics

Personal

Free
Only one user
Sign up
What’s included:
  • SaaS hosting

  • 1 member

  • 100 GB storage included

  • Unlimited experiments

  • Public and private projects

  • Email and chat support

Seed Startup

$35.00/mo
Per user (seed stage startups only)
Start a free trial
All of Personal plus:
  • Up to 3 team members

  • 1 collaborative team

Team

$200.00/mo
Per user
Start a free trial
All of Startup plus:
  • Up to 5 team members

  • 1 collaborative team

Enterprise

Contact Us
Custom plans available
Contact sales
All of Team plus:
  • SaaS or Private Cloud

  • Unlimited members

  • Custom storage

  • Multiple collaborative teams

  • Single sign on

  • Support SLA

  • Service account

Academic research teams

Use W&B to coordinate projects remotely, like Google Drive for machine learning.

create a team

Self-hosting with W&B Local

Need HIPAA compliance, or have data that can't leave your system? Install Weights & Biases locally, on your own servers.

Try local

Why use Weights & Biases?

1
Automatic Tracking

Tired of pasting results into a spreadsheet? Track models automatically.

2
Model Debugging

Debug model issues quickly with logs, charts, and tables of tracked results.

3
Collaboration

Quickly share findings and discuss model results with your team.

4
Reproducibility

Use W&B’s reliable system of record to make all models reproducible.

Proudly supporting 80,000+ ML practitioners at 200+ companies and institutions

Compare plans

Personal

Free

Seed Startup

$35.00/mo per user

Team

$200.00/mo per user

Enterprise

Contact our sales team

Dashboard: Experiment tracking

Hosting and installation

Self-host your own installation of W&B for complete control of where data is visualized and stored.

ML project management
Unlimited
Unlimited
Unlimited
Unlimited
Team management

Use W&B Teams to privately collaborate on ML projects and share results internally.

1
1
Unlimited
Up to 3
Up to 5
Unlimited

Artifacts: Data versioning

Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes.

Click through the UI to explore the relationship between a given dataset and the models in your pipeline, or identify all the precursor steps to a model you currently have in production.

Log and visualize predictions across models to identify issues in training, or find commonly misclassified examples that might need to be relabeled.

Sweeps: Hyperparameter optimization

Default sweeps dashboards are populated with a parallel coordinates chart and parameter importance panel, giving you fast access to insights about what hyperparameter combinations are the most effective.

Run automated sweeps to quickly find the best hyperparameters for your models, and use advanced features like early stopping to save compute resources.

Dashboard: Experiment tracking

Hosting and installation

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ML project management

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Unlimited
Team management

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Artifacts: Data versioning

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Sweeps: Hyperparameter optimization

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10

Dashboard: Experiment tracking

Hosting and installation

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ML project management
Unlimited
Team management

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1
Up to 3

Artifacts: Data versioning

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Sweeps: Hyperparameter optimization

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10

Dashboard: Experiment tracking

Hosting and installation

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ML project management
Unlimited
Team management

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1
Up to 5

Artifacts: Data versioning

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Sweeps: Hyperparameter optimization

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50

Dashboard: Experiment tracking

Hosting and installation

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ML project management
Unlimited
Team management

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Unlimited
Unlimited

Artifacts: Data versioning

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Sweeps: Hyperparameter optimization

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Unlimited

Lineage & Tracking

You have complete control of what is logged in W&B. You can curate and delete data from the system at any time.

Storage

The hosting costs of files saved to W&B servers

  • 100 GB included free

  • $0.08 per GB up to 10 TB

  • $0.06 per GB up to 100 TB

  • $0.05 per GB up to 1000 TB

Artifacts

Files explicitly tracked with artifacts for reproducibility

  • 100 GB included free

  • $0.05 per GB up to 10 TB

  • $0.03 per GB up to 100 TB

  • $0.02 per GB up to 1000 TB

Custom plans available.

Contact sales

Key Values of
Weights & Biases

How our lightweight, interoperable tools empower your ML team

Reliable, automatic experiment tracking

Develop better models faster

Enable faster model development

Use the single, central system of record to save all the relevant metadata for your models automatically, so you can focus on model training. Spend more time in a flow state.

Debug model training seamlessly

Use live dashboards, with system metrics and terminal logs for each experiment, to understand bottlenecks and debug model training quickly, without hopping between tools.

Compare the latest results to previous baseline

Quickly understand what architecture and hyperparameter choices work, and focus on training new models. Avoid slowly digging through scattered files of manually-tracked results.

Key insights from experiment tracking

  • How are the latest experiments doing, compared to previous model versions?

  • Is this model running out of GPU memory? What are the system bottlenecks?

  • How does this hyperparameter affect accuracy across different classes?

  • What do sample predictions look like from this model?

Persistent knowledge base

Capture valuable insights centrally

A lab notebook for every ML experiment

Every time you run a new experiment, W&B captures the changes you made and gives you a place to jot down notes. Quickly compare your latest results with previous baselines.

Reproduce any experiment

Automatically track the exact version of the code, hyperparameters, and even the dataset your model trained on. Trace back exactly where your resulting models came from.

Transparent, understandable results

Annotate findings inside the central W&B system, so your models can speak for themselves. Use interactive notes, comments, and reports to clearly explain your research.

Key insights from a persistent knowledge base

  • Where are all the model files stored for this project?

  • What have we tried already, and what avenues should we explore next for improving this model?

  • What are the key findings from this research?

Machine learning team management

Move quickly on big projects, and make handoffs seamless

Easily track anywhere, on any infrastructure

Train on whatever compute is available - AWS, GCP, Azure, or the GPU box in the office. All results are organized in a central place.

Modular, interoperable solutions

Pick the tools that solve your problems best. With W&B, you're not locked in to an end-to-end platform. We pride ourselves on making our tools play well with other systems.

Stay focused on the hard problems

Spend more time in a flow state, and less time doing tedious tracking. With W&B, you can focus on the hard machine learning problems, and let us take care of tracking all the details to make the models reproducible.

Key insights from team management

  • How should new team members get started contributing to this ML project?

  • When someone leaves the team, how is their research saved and communicated?

  • How are all our projects doing, across the organization? Are any particular projects roadblocked or stuck? Are certain projects performing better than expected?

Production model lineage

Version and track every artifact in your model pipeline

Reproducibility at scale

Know where a model comes from end to end, and what is running where.

Dataset versioning

  • Reliably capture all changes to the data you're using to train your models.

  • Save datasets in any system: GCP, AWS, Azure, or even uploaded directly to W&B servers.

  • Clearly identify what downstream steps in your pipeline were affected by a change in data.

Model management

  • Capture all of your trained models in one central system.

  • Maintain a clear picture of all the data, preprocessing, and evaluation that was done on each model.

  • Trace back the lineage of any production model to the exact code and data that it was trained on.

Key insights from artifacts

  • What data was this production model trained on?

  • How did relabeling a chunk of data affect the accuracy of this model?

  • This data was corrupted — what downstream models were affected by this issue?

  • How did changing this preprocessing step affect model accuracy?