Hugging Face
A shared home for finding, publishing, and using machine learning models, datasets, and applications.
About Hugging Face
Hugging Face is a platform where machine learning teams and the open source community publish, discover, evaluate, and collaborate on models, datasets, and interactive applications. The Hub uses Git based repositories and adds ML specific tools such as model evaluation and dataset viewing. Developers can browse a large public catalog, share their own artifacts, build Spaces, and connect code to hosted resources. The Datasets library loads and streams audio, computer vision, and NLP data, while Accelerate adapts PyTorch training and inference code to distributed configurations, including DeepSpeed, fully sharded data parallelism, and mixed precision. Inference Providers adds access to supported models from multiple providers, with model level information such as price, context size, latency, throughput, tool use, and structured output support. The tradeoffs are practical: private collaboration and account features can add per user fees, storage is metered by volume, inference costs vary by provider and model, and teams must review the licenses and documentation attached to community assets before production use.
Best for
Best use cases
Compare community models and provider metrics before choosing an inference route
Prepare datasets and distributed PyTorch workloads with Hugging Face libraries
Publish models, datasets, or Spaces for internal or public collaboration
Compare before choosing
Free public Hub access lowers the entry cost, while private or advanced workflows may require paid plans
The catalog spans models, datasets, and apps, while suitability depends on each community asset's license, documentation, and quality
Hosted inference offers provider choice, while price and capabilities vary by model and provider
Features
Discover and evaluate models
Search the Hub catalog, inspect model pages, compare supported inference options, and test candidates before integrating them.
Share and process datasets
Publish datasets or load them through the Datasets library, including streaming and Apache Arrow based processing.
Scale PyTorch workloads
Use Accelerate to prepare existing training or inference code for distributed systems, mixed precision, DeepSpeed, or FSDP.
Pros
- Large public catalog connects models, datasets, and Spaces in one workflow.
- Free public access keeps initial discovery and sharing costs low.
- Datasets and Accelerate integrate Hub resources with common Python and PyTorch workflows.
Cons
- Paid account and organization tiers add recurring per user costs.
- Storage and inference can create variable bills as data volume and usage grow.
- Community assets differ in licenses, documentation, and quality, so production review remains necessary.
Pricing Plans
Free Hub
- Browse and publish public models, datasets, and Spaces
- Use Git based collaboration on the Hub
- Access ML tools such as dataset viewing and model evaluation
Pro
- Paid plan for a personal account
- Access to advanced platform capabilities
- Separate usage and storage charges may apply
Team
- Paid collaboration plan for organizations
- Available by credit card
- Per user billing
Enterprise
- Organization plan sold through sales
- Per user billing
- Enterprise support is available separately
Hub storage
- Egress and CDN included at no extra cost
- Volume discounts begin at 50 TB
- Custom pricing available above 500 TB
* Pricing information is for reference only. Please refer to the latest announcement on Hugging Face's official website.