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HuggingFace

Hugging Face is a collaboration platform where the machine-learning community discovers, develops, and shares models, datasets, and applications.

Developer platformsProductivityModel & API platformsProductivityAI learningPaid
Official website

Plans and pricing

Official pricing verified
Access
Paid
Paid plans from
$9/month
Billing
Subscription + credits

PRO Account

$9/month

Team

$20/month per user

Enterprise

$50/month per user

Official pricing

Official sources

HuggingFace official websiteHuggingFace pricingHuggingFace official website

Verified: September 8, 2026

DevPrice organizes public information and does not sell HuggingFace subscriptions. Prices and availability are determined by HuggingFace.

What is HuggingFace?

Hugging Face is a collaboration platform where the machine-learning community discovers, develops, and shares models, datasets, and applications. Researchers, developers, and technical teams can work across text, image, audio, video, and 3D projects while publishing resources and building a visible portfolio of their work.

The platform combines browser-based repositories and Spaces with open-source libraries such as Transformers, Diffusers, Datasets, and Tokenizers, plus hosted inference and compute services. Public resources can be explored on the website, but publishing projects, maintaining a profile, creating an application, or accessing controlled team resources generally requires an account; running a model may also require a local development setup, an API integration, or separately configured compute.

Key features of HuggingFace

  • Model and dataset hub

    The hub hosts community models and datasets that can be explored by task or modality, with project pages exposing documentation and associated files for research, prototyping, and technical selection.

  • Spaces application hosting

    Spaces hosts shareable machine-learning applications and interactive demonstrations, turning model behavior into an accessible web experience. Creating and maintaining a Space requires an account, and its compute needs depend on the application configuration.

  • Open-source tooling

    Libraries including Transformers, Diffusers, Datasets, and Tokenizers support model use, diffusion workflows, data access, and tokenization. They are intended for developers working in code environments to train, fine-tune, or integrate models.

  • Inference and deployment

    Inference Providers expose different models through a unified API, while Inference Endpoints deploy optimized services and Spaces can be assigned additional compute. These paths require the relevant API, account, and runtime configuration.