Hugging Face Hub

What it does

Hugging Face Hub is a hosted platform where people store, version, and share machine learning models, datasets, and applications, built on Git-based repositories. Visitors can browse millions of models, hundreds of thousands of datasets, and a large catalogue of Spaces, which are hosted demos and apps. Hosted GPU hardware and dedicated inference endpoints are available for running models, and the Hub connects to Inference Providers offering access to tens of thousands of models through one API.

Teams typically use it to publish models and datasets, collaborate on repositories, experiment with community models in Spaces, and deploy models on managed infrastructure. Its distinct features include built-in dataset viewing, model evaluation tooling, and an open-source library ecosystem including Transformers, Diffusers, and PEFT that integrates with the Hub.

The free account covers public repositories and basic use, with Pro at $9 per month for individuals and Team at $20 per user per month for organizations. Enterprise pricing is custom and adds single sign-on, audit logs, storage region selection, and priority support. Paid compute such as GPU hardware is billed by the hour, and storage beyond included quotas is billed per terabyte per month.

What it can do

• Host and version public and private model, dataset, and Space repositories with Git-based collaboration
• Browse and discover models, datasets, and Spaces across modalities including text, image, video, audio, and 3D
• Run community-built ML demos and apps in hosted Spaces, with GPU upgrades
• Deploy models on dedicated, autoscaling Inference Endpoints
• Access tens of thousands of models through a unified Inference Providers API
• Use open-source libraries such as Transformers, Diffusers, and PEFT alongside the Hub

Works with

• AWS Marketplace billing for Enterprise subscriptions

Example workflows

Publish and share a model

Input: a trained model's files. Steps: create a model repository, push files via Git or the Python Hub library, add a model card. Output: a public or private repository others can browse and download.

Deploy a model on dedicated endpoints

Input: a model repository on the Hub. Steps: choose an Inference Endpoint with CPU or GPU hardware, select autoscaling settings, deploy. Output: a dedicated HTTPS inference endpoint billed by the hour.

Before you choose

• Compute and GPU hardware are billed hourly and can add up quickly for always-on endpoints.
• Private storage and public storage have different included quotas, and extra storage is billed per terabyte per month.
• Enterprise features such as SSO, audit logs, and storage regions are only described for Enterprise plans.

Listed under

• Coding
• Sandboxes and infrastructure
• AI apps
• AI models
• Collaboration
• Datasets
• Developers
• Machine learning

Get started

Create an account at the sign-up page to begin hosting and browsing.

1. Create a free account at the sign-up page.
2. Install the Hub Python library and log in with an access token.
3. Create a model or dataset repository and push files via Git or the Python library.

Team and Pro plans are subscriptions. Compute is billed hourly per hardware type, storage per terabyte per month beyond included quotas. Inference Providers API has no service fees per the page; Pro includes $2 of monthly inference credits.

Source: huggingface.co/pricing, checked 28 Oct 2026. Prices can change; check before you buy.

Sources for this overview

Product pages, documentation, and pricing checked 28 Oct 2026. Each source supports part of the overview; availability and prices can change.

• Hugging Face – The AI community building the future. ↗huggingface.co
• Pricing - Hugging Face ↗huggingface.co
• Team & Enterprise Plans - Hugging Face ↗huggingface.co

Company website

huggingface.co: product, pricing and policy pages

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knowledge_base

Last checked 28 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.