Hugging Face

The open-source platform where the world's AI models, datasets, and demos live

Hugging Face is a Paris-founded, New York-headquartered AI platform company that has become the default public repository and collaboration layer for machine learning — often described as “GitHub for AI.” As of early 2026, the Hub hosts 2.4M+ models spanning text, vision, audio, video, and multimodal tasks, along with 730K+ datasets and roughly one million Spaces (interactive demo applications). Founded in 2016 and valued at approximately $4.5B, the platform serves over 10,000 enterprise customers including Google, Microsoft, Amazon, and Intel, alongside millions of individual researchers and developers. Nearly every open-weight model — from Meta’s Llama to Mistral to Google’s Gemma — is distributed primarily through the Hub, making Hugging Face the upstream source of truth for the global AI supply chain. Over 30% of the Fortune 500 now maintain verified accounts on Hugging Face. Hugging Face also acquired Pollen Robotics in April 2025, extending its platform into robotics data and hardware.

Compliance

GDPR SOC 2

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Key Features

Hugging Face Hub (model and dataset registry): The central repository hosting 2.4M+ models and 730K+ datasets with versioning, model cards documenting training data and intended use, license metadata, and access controls — the canonical discovery and distribution layer for open-weight AI.
Transformers library (160K+ GitHub stars): The foundational open-source Python library providing a unified API for loading, fine-tuning, and running thousands of model architectures across PyTorch, TensorFlow, and JAX — the standard toolkit for ML practitioners worldwide.
Inference Endpoints: A one-click deployment service for any model from the Hub to a private AWS or Azure GPU instance with no DevOps required, now offering fully managed infrastructure with autoscaling and scale-to-zero — the fastest path from a Hub model to a private production API.
Inference Providers: A single OpenAI-compatible API that routes to 15+ third-party providers including Groq, Together AI, Fireworks, Cerebras, and others — giving teams access to optimized inference hardware through one integration without vendor-specific SDK rewrites.
Spaces (hosted AI demos): A hosting platform for interactive ML applications built with Gradio, Streamlit, or static HTML, backed by ZeroGPU (free shared GPU compute now running NVIDIA H200 Blackwell GPUs) — used for sharing demos, prototypes, and internal AI tools.
Enterprise Hub: Private repositories, SAML SSO, RBAC, audit logs, EU data residency, organization-level access controls, and enhanced rate limits for enterprise teams requiring security and governance on top of the open platform.

Use Cases

For ML teams finding and deploying open-weight models: An engineering team evaluates Llama, Mistral, and Gemma variants for a specific business task on the Hub, compares license terms and model cards, and deploys the selected model to a private Inference Endpoint in a day.
For enterprises building private AI applications: A financial services firm uses Enterprise Hub to maintain a private catalog of fine-tuned models with access controls, deploying them to private endpoints that keep proprietary data within their cloud region.
For researchers and developers sharing AI work: A research team publishes model weights, datasets, and a Spaces demo on the Hub to support reproducibility and community adoption of their work — the standard publication channel for open AI research.

Pricing MODELS

Freemium

Pricing Summary

Free tier with generous Hub access, public Spaces, and limited Inference API calls. Pro at $9/month for individuals (more compute, higher rate limits). Enterprise Hub at $20/seat/month, adding private repositories, SSO, RBAC, audit logs, and EU data residency. Inference Endpoints and Inference Providers are separately billed on usage.