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Pricing

Build cutting-edge models in half the time

Free teams available for academics
Cloud-Hosted
Private Hosting

Personal

Free
Personal accounts
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  • For personal projects only. Corporate use not allowed, must use Starter or Enterprise plans.

  • Unlimited experiments

  • Unlimited tracked hours*

  • 100 GB storage and artifacts tracking included. For additional storage, see prices.

  • W&B Launch on Servers

Starter

$50 / mo
Per user, billed annually
SIGN UP + CREATE TEAM

First 250 tracked hours* free

  • Tier 1: $50/user/mo from 250 to 5000 cumulative tracked hours*

  • Tier 2: $100/user/mo from 5,000 to 10,000 cumulative tracked hours*

  • Tier 3: $150/user/mo from 10,000 to 15,000 cumulative tracked hours*

  • Up to one team and 10 users per organization

  • Email and chat support

  • 100 GB storage and artifacts tracking included. For additional storage, see prices.

Enterprise

Contact Us
Custom plans available
Contact sales

  • Flexible deployment options

  • Unlimited tracked hours* tracked

  • Dedicated Machine Learning Engineer & CSM to ensure success

  • Dedicated support channel

  • Support SLA

  • Custom storage plan

  • Single sign on

  • Service account for CI workflows

Personal

Free
Personal accounts
1 pip install wandb
2 wandb server start
Try now with two lines of code!

  • For personal projects only. Corporate use not allowed.

  • Unlimited experiments

  • Unlimited tracked hours*

  • 100 GB storage and artifacts tracking included.  For additional storage, see prices

  • Run a W&B server locally on any machine with Docker and Python installed


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get started

Enterprise

Contact Us
Custom plans available
1 pip install wandb
2 wandb server start
Try now with two lines of code & a free Enterprise Trial license

  • Flexible deployment options

  • Unlimited tracked hours* tracked

  • Dedicated Technical Account Manager to ensure success

  • Dedicated support channel

  • Support SLA

  • Custom storage plan

  • Service account for CI workflows

  • Run a W&B server locally on your own infrastructure with a free enterprise trial license

GET STARTED
*What is a tracked hour?: A tracked hour is wall-clock time when training a model. If your model takes 8 hours to train, you have used 8 tracked hours. Today, this is irrespective of GPU quality, speed, etc. Cumulative tracked hours counted per organization.
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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 100,000+ ML practitioners at 200+ companies and institutions

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?