automation

Hugging Face

Hosting platform for machine learning models, datasets and apps, with community sharing and paid team plans.

How the 8.2 was reached

  1. Quality of output8/10

    2M+ models and 300M+ downloads reported independently; broad community contributions across universities, nonprofits and companies. aiworld.eu

  2. Ease of use9/10

    Free signup with unlimited public models, datasets and apps hosting. huggingface.co

  3. Pricing value8/10

    Useful free tier with 100 GB storage; PRO $9/month, Team $20/user, Enterprise $50/user. metacto.com

  4. API and integration quality8/10

    Python and JavaScript SDKs with authenticated API and documented reference guides. huggingface.co

  5. Solves the problem it claims to solve8/10

    Hosts models, datasets and apps across many modalities and is widely used by ML researchers, matching the community-platform claim. allthingsopen.org

8.2/10 is the mean of the 5 criteria that apply. Scored on the five-criteria rubric v1, 29 September 2026. How scoring works

Pricing

Free
$0
  • Unlimited public models, datasets and apps
  • 100 GB storage
PRO
$9/month
    Team
    $20/user/month
    • SSO
    • Regions
    • Priority support
    Enterprise
    $50/user/month

      Key Features

      • Hosting for models, datasets and applications
      • Support for text, image, video, audio and 3D modalities
      • Python and JavaScript SDKs with an authenticated API
      • 2M+ models, 500k+ datasets and 1M+ apps on the platform
      • Team and Enterprise plans with SSO and priority support

      Pros & Cons

      Pros

      • Free signup with unlimited public hosting
      • Very large catalogue of models, datasets and apps
      • Python and JavaScript SDKs with reference docs
      • Paid tiers start at a low $9 a month

      Cons

      • Free storage is capped at 100 GB
      • Pricing page text was not found, so plan details rest on secondary facts
      • Quality of community uploads varies
      Verdict

      A strong pick for ML researchers and teams who want to share or find models and datasets. Teams that need strict private infrastructure should check the Enterprise terms closely before committing.

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