Hugging Face

Freemium

By AI Bookmarks Editorial Team, Directory editors Last reviewed 2026-10-23

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Open platform for hosting and running models and datasets, free for public work with paid compute and enterprise tiers.

Quick facts

Pricing	Freemium
Free tier	Yes — free for public repositories, datasets and demos
Platforms	Web, API, Python libraries
Developer	Hugging Face
Website	https://huggingface.co/

What Hugging Face is

Hugging Face is a platform for sharing and running machine-learning models. Its repositories host model weights, datasets and demo applications, with libraries such as Transformers and Diffusers providing the code to load them. For most teams it is where a model is found before it is used elsewhere: you check the model card, the licence and the reported benchmarks, then download the weights or call an inference endpoint. It also offers hosted inference, training jobs and demo spaces, so a prototype can run without local GPUs. The limits are governance and quality: model cards vary in detail, licences differ per repository and some restrict commercial use, and the open catalogue includes models that are poorly documented or unsafe to deploy without review. Spaces can host a demo from a repository in minutes, which is how many models get their first public try. A model page links the paper, the dataset and the evaluation results when the uploader provides them.

Best for

Finding and comparing open models before adoption
Hosting a demo without provisioning GPUs
Teams that need to self-host rather than call an API

Not ideal for

Users who want a finished product rather than model weights
Deployments without anyone to review licences and safety

Pros

Largest catalogue of open models and datasets
Libraries make loading a model straightforward
Free hosting for public demos

Cons

Licences differ per repository and can restrict commercial use
Model quality and documentation vary widely
Running at scale requires your own infrastructure

Pricing in detail

Public repositories, datasets and demo spaces are free. Paid plans cover private repositories, higher inference quotas and dedicated compute, billed monthly per organisation plus usage for GPU time; enterprise plans add single sign-on, audit logs and support. Because compute is metered by the hour, cost depends on how much you train or serve rather than on how many repositories you keep.