Hugging Face

Hugging Face is the default public registry for open machine-learning models, datasets and demo applications, plus the libraries and hosted inference that put them into production. Engineering teams use it to find and serve open-weight models, and enterprises pay for private storage, governance controls and managed GPU endpoints on the same platform.

Hugging Face is the de facto public registry of open machine learning. Its Hub hosts more than two million models and 500,000 datasets by the company's own count, alongside over a million interactive Spaces applications, and is used by more than 50,000 organisations including Google and Meta, who publish their open-weight releases there directly. Around it sits the open-source stack most practitioners actually write against — Transformers, at roughly 166,000 GitHub stars, plus Diffusers, Datasets and TRL for reinforcement-learning training — which is the real source of the platform's gravity: the library is the industry's default API for loading a model, and the Hub is where that API points. Commercially it monetises through subscriptions and compute rather than the models themselves. PRO at $9 a month raises storage and inference quotas for individuals; Team at $20 per user and Enterprise at $50 per user add SAML and OIDC single sign-on, audit logs, resource-group access control, SCIM provisioning and Storage Regions for data-location control. Hosted compute is metered separately, with Spaces GPU hardware from free through to $23.50 an hour and Inference Endpoints from $0.033 an hour on CPU to $74.00 an hour on an NVIDIA B200, while Inference Providers routes a unified API to more than 45,000 models with no service fee on top. Independent estimates put annual recurring revenue near $150 million by August 2026, up from roughly $81 million at the end of 2025. On 3 September 2026 NVIDIA announced it would acquire the company for $12.93 billion.

Category

AI Models & APIs

Pricing

Freemium, Subscription, Usage-based

Target Market

Data Scientists, Enterprise Developers, MLOps Engineers, CTOs, AI Researchers

Deployment

Cloud-first, API-based, Open-source, Multi-cloud

Customers

200,000+ companies and 18M+ developers per NVIDIA's September 2026 acquisition announcement; 50,000+ organisations per Hugging Face

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  The Hub

  A versioned registry of over two million models and 500,000 datasets with git-backed repositories, used by more than 50,000 organisations including Google and Meta.

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  Transformers and the open-source stack

  Transformers at roughly 166,000 GitHub stars, plus Diffusers, Datasets and TRL, which is the default API practitioners use to load and fine-tune models.

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  Spaces

  More than a million hosted interactive applications running Gradio or Docker, with GPU hardware available from free ZeroGPU tiers up to eight-way L40S.

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  Inference Providers

  A unified API routing to more than 45,000 models across multiple third-party vendors with no service fee added on top of the provider's own cost.

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  Inference Endpoints

  Managed dedicated model deployment billed hourly from $0.033 on CPU to $74.00 on an NVIDIA B200, so a chosen model can be served without running GPUs yourself.

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  Enterprise governance controls

  SAML and OIDC single sign-on, audit logs, resource groups for granular access control, SCIM user provisioning and Storage Regions for data-location control.

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  Open-weight model selection

  Compare candidate open models on the Hub, test them in a Space, then deploy the winner to a dedicated endpoint without changing libraries.

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  Private enterprise model registry

  Store fine-tuned internal models in private repositories with audit logs and resource-group permissions instead of passing checkpoints around object storage.

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  Fine-tuning and preference training

  Use TRL and Datasets to fine-tune an open base model on proprietary data and publish the result back to a private organisation repository.

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  Multi-provider inference routing

  Call many hosted model providers through one API surface so switching provider or model does not mean rewriting integration code.

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  Data-resident deployments

  Pin repositories to a chosen storage region and enforce SSO so regulated teams can adopt open models within their jurisdictional constraints.

Seats and compute are billed separately and both matter. Seat tiers are PRO at $9 a month for an individual, Team at $20 per user a month and Enterprise at $50 per user a month, with single sign-on, Storage Regions and audit logs starting at Team — so the governance features most enterprises actually need are not available on PRO at any price. Compute is metered hourly and spans a very wide range: Spaces GPU hardware runs from free CPU and ZeroGPU tiers up to $23.50 an hour for eight NVIDIA L40S, and Inference Endpoints from $0.033 an hour on CPU to $74.00 an hour on an NVIDIA B200. Hub storage is charged per terabyte per month, roughly $8-12 for public repositories scaling down to $8 above 500TB, and $12-18 for private ones. Inference Providers adds no service fee on top of the underlying provider price. Independent analysis notes the platform has historically been under-monetised relative to its usage, with revenue shifting away from one-off consulting toward recurring API and partnership fees.

This page was written from 5 sources, 3 on domains other than huggingface.co.

1. 1.huggingface.co — huggingface.covendor
2. 2.huggingface.co — pricingvendor
3. 3.blogs.nvidia.com — nvidia to acquire hugging face
4. 4.sacra.com — hugging face
5. 5.hn.algolia.com — search