AutoTrain documentation

How much does it cost?

You are viewing v0.8.21 version. A newer version v0.8.24 is available.
Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

How much does it cost?

AutoTrain offers an accessible approach to model training, providing deployable models with just a few clicks. Understanding the cost involved is essential to planning and executing your projects efficiently.

Local Usage

When you choose to use AutoTrain locally on your own hardware, there is no cost. This option is ideal for those who prefer to manage their own infrastructure and do not require the scalability that cloud resources offer.

Using AutoTrain on Hugging Face Spaces

Pay-As-You-Go: Costs for using AutoTrain in Hugging Face Spaces are based on the computing resources you consume. This flexible pricing structure ensures you only pay for what you use, making it cost-effective and scalable for projects of any size.

Ownership and Portability: Unlike some other platforms, AutoTrain does not retain ownership of your models. Once training is complete, you are free to download and deploy your models wherever you choose, providing flexibility and control over your all your assets.

Pricing Details

Resource-Based Billing: Charges are accrued per minute according to the type of hardware utilized during training. This means you can scale your resource usage based on the complexity and needs of your projects.

For a detailed breakdown of the costs associated with using Hugging Face Spaces, please refer to the pricing section on our website.

To access the paid features of AutoTrain, you must have a valid payment method on file. You can manage your payment options and view your billing information in the billing section of your Hugging Face account settings.

By offering both free and flexible paid options, AutoTrain ensures that users can choose the most suitable model training solution for their needs, whether they are experimenting on a local machine or scaling up operations on Hugging Face Spaces.

< > Update on GitHub