Hugging Face

What is Hugging Face?

Hugging Face is an open platform for sharing and running machine learning models, datasets and demo apps. Its Hub hosts repositories, Spaces runs hosted demos, and Inference Endpoints deploys models on managed hardware, with open-source libraries tying the workflow together.

Key capabilities of Hugging Face

• Model and dataset Hub: hosts public and private repositories with a dataset viewer
• Spaces: runs hosted demo apps on free CPU or paid GPU hardware
• Inference Endpoints: deploys models on dedicated CPU and GPU instances
• Inference providers: PRO and Team members receive inference credits for provider access
• ZeroGPU: shared GPU quota for Spaces, higher on PRO

How Hugging Face works

Users create an account, then push models or datasets to repositories and build Spaces to demo them. For production inference, Inference Endpoints runs a chosen model on dedicated hardware billed by the hour. Storage on the Hub is priced per terabyte, with private repositories costing more than public ones. Organizations use the Team or Enterprise plans for SSO and access control.

Who uses Hugging Face?

Machine learning engineers, researchers, data scientists and enterprises use it to publish and discover open models, run demos and host inference. Enterprise adds SCIM, dedicated support and legal and compliance processes for larger organizations.

Hugging Face pricing

The basic account is free. PRO costs $9 a month, Team $20 per user a month and Enterprise $50 per user a month. Hub storage runs $8 to $12 per TB for public repos and $12 to $18 per TB for private. Inference Endpoints start at $0.033 an hour; Spaces GPUs reach $23.50 an hour.

Hugging Face alternatives

Alternatives for MLOps and model hosting include DataRobot for enterprise AutoML and governance, Dataiku for collaborative analytics, Domino Data Lab for model operations, and Google Vertex AI for cloud-managed ML.