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Hugging Face

Hugging Face

The open-source AI hub — build, share, and deploy models.

Model & API PlatformAPICodingResearchTrial

Hugging Face is the leading open-source AI platform hosting over 1 million pre-trained models, datasets, and Spaces (AI demo apps). It provides tools for training, fine-tuning, deploying, and sharing machine learning models — with built-in Inference API, AutoTrain, and a vibrant community driving open science.

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Updated

2026-06-10

Platforms

Web / API

Freemium

See the official site for current limits and plans.

* Pricing is for reference only; see the official site for current pricing.

Hugging Face
huggingface.co

Key Features

  • Model Hub

    Browse and discover over 1 million pre-trained models covering NLP, computer vision, audio processing, multimodal AI, and reinforcement learning.

  • Inference API

    Deploy and serve thousands of models with a simple API — no infrastructure management needed. Scale from prototype to production.

  • AutoTrain

    Fine-tune state-of-the-art models on your own dataset through an automated no-code interface. Supports text, image, and tabular data.

  • Spaces

    Create and host interactive AI demo apps using Gradio or Streamlit. Share them with the community or embed in your projects.

  • Datasets

    Access over 100,000 curated datasets for training and evaluation, with built-in preprocessing pipelines and streaming support.

  • Open Source Community

    Join a vibrant community of researchers and engineers. Collaborate on model development, share knowledge, and advance open science.

Best For

Machine learning developers

Use pretrained models and Inference APIs to build and deploy machine learning applications.

AI researchers

Use curated datasets and open collaboration to train, fine-tune, and share models.

AI demo creators

Host and share interactive AI demo applications through Spaces.

Pros

  • World's largest open-source model repository
  • Free Inference API with generous credits for experimentation
  • Supports PyTorch, TensorFlow, JAX, and more
  • Comprehensive ecosystem — models, datasets, Spaces, libraries

Considerations

  • Self-hosting large models requires significant GPU resources
  • Free Inference API has rate limits for production use
  • Model quality and documentation vary across community uploads
  • Not all models have clear licensing terms

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