Hugging Face

Hugging Face is a platform for open machine learning. Its Hub hosts more than 2 million models, 1.5 million datasets and 1.5 million AI apps, called Spaces, and the company also maintains widely used open-source libraries such as Transformers and Datasets.

It solves the problem of finding, sharing and running machine learning models. Teams can discover a model with its model card, try it in the browser, download it into their own code, or run it through hosted inference, all from the same place, and keep private models and datasets for internal work.

Key features

• Git-based repositories. Models, datasets and Spaces live in version-controlled repositories with commit history, branches and diffs, backed by the Xet storage system for large files.
• Model and dataset cards. Cards document a model's or dataset's purpose, limitations and biases, and Data Studio lets you explore datasets in the browser.
• Spaces. Host demo apps built with Gradio, Streamlit, static HTML or Docker, with ZeroGPU giving GPUs on demand.
• Inference Providers. One API gives access to more than 200 models from partner inference providers, billed at provider rates with no Hugging Face markup.
• Inference Endpoints and Jobs. Deploy models on dedicated hardware, or run training and batch jobs on Hugging Face compute.
• Storage Buckets. S3-like object storage for checkpoints, logs and other large files that do not need version control.
• Organisations and security. Organisations, access tokens, resource groups, SSO, audit logs and storage regions support team use.

Who it is for

Hugging Face serves machine learning engineers, researchers and data scientists, as well as application developers who want to use open models without training their own. Startups use it to publish demos and deploy models; larger organisations use Team and Enterprise plans to manage private models, control access and keep data in chosen storage regions.

Pricing at a glance

The Free plan costs $0 and covers public repositories and free CPU Spaces. PRO costs $9 per month with 10x private storage, 8x ZeroGPU quota and $2 of monthly compute credits. Team costs $20 per user per month and adds SSO, storage regions, resource groups and usage analytics. Enterprise costs $50 per user per month with the highest limits, SCIM and dedicated support. Extra storage costs from $12 per TB per month for public and $18 per TB per month for private data, with discounts at volume. Spaces hardware ranges from free to $23.50 per hour, and Inference Endpoints from $0.03 to $74 per hour. Prices as checked on 7 October 2026; confirm on the vendor's pricing page.

Things to consider

• Free accounts get no monthly inference credits and must buy credits to use Inference Providers.
• Compute is usage-based, so GPU Spaces and endpoints left running can grow costs; organisations can set spending limits.
• Each model has its own licence; check the model card before using a model commercially.
• SSO, storage regions and resource groups require Team or Enterprise.

Getting started

1. Create an account at huggingface.co and browse models for your task.
2. Read the model card and licence, then try the model in its widget or a Space.
3. Create an access token and call the model through Inference Providers, or download it with the libraries.
4. Read the Hub documentation before publishing your own model or Space.