About Hugging Face
Hugging Face hosts 1M+ open models, datasets, and Spaces. The de-facto home of open-source AI. Hugging Face stands out for model hosting and discovery, dataset repository and sharing, spaces for applications, and inference endpoints for deployment. Teams pick it because massive open model library and spaces for instant demos. Things to keep in mind: self-serve setup needs skill.
Hugging Face is a freemium tool and launched on 2016-12-01. It sits in the Coding and Research space and is best used to Collaborating on machine learning models and datasets, Deploying AI applications with GPU acceleration, Building and sharing ML portfolios.
Pricing
Pricing model
Freemium subscription
Currency
USD
Payment options
Monthly
FreeFree
- Host unlimited public models, datasets, and applications
- Access to open-source tools
- Community collaboration
Team & Enterprise$20/user/month
monthly
- Single Sign-On
- Regions
- Priority Support
- Audit Logs
- Resource Groups
- Private Datasets Viewer
Inference Endpoints$0.60/hour
hourly
- GPU compute
- Deploy optimized inference endpoints
- Update Spaces applications to GPU
Features
- •Model hosting and discoveryBuilt-in support for model hosting and discovery — used for collaborating on machine learning models and datasets.
- •Dataset repository and sharingBuilt-in support for dataset repository and sharing — used for collaborating on machine learning models and datasets.
- •Spaces for applicationsBuilt-in support for spaces for applications — used for collaborating on machine learning models and datasets.
- •Inference Endpoints for deploymentBuilt-in support for inference endpoints for deployment — used for collaborating on machine learning models and datasets.
- •GPU compute resourcesBuilt-in support for gpu compute resources — used for collaborating on machine learning models and datasets.
- •Inference Providers with 45,000+ modelsBuilt-in support for inference providers with 45,000+ models — used for collaborating on machine learning models and datasets.
- •Storage BucketsBuilt-in support for storage buckets — used for collaborating on machine learning models and datasets.
- •Open-source ML toolingBuilt-in support for open-source ml tooling — used for collaborating on machine learning models and datasets.
Pros
- Massive open model library
- Spaces for instant demos
- Active research community
Cons
- Self-serve setup needs skill
- Pro tier needed for serious compute
- Documentation varies wildly
Categories
Coding
Research
Machine Learning
Model Repository
AI Development Platform
Data Sharing
Best use cases
Collaborating on machine learning models and datasets
Deploying AI applications with GPU acceleration
Building and sharing ML portfolios
Accessing pre-trained models across multiple modalities
Enterprise AI development with security and access controls
Running inference at scale through unified API
Frequently Asked Questions
General
Pricing
Features
Beginner
Advanced
API
Integrations
Security
Related & Connected
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