Hugging Face
Hugging Face hosts 2M+ open models plus datasets and Spaces demos, with PRO, Team and Enterprise plans and pay-as-you-go GPUs.
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Overview
Hugging Face is where much of the machine learning world shares its work. The Hub hosts more than 2 million models, along with datasets and Spaces, which are small apps and demos that run in the browser. Many labs publish open model weights here; the trending list during our check included new releases from DeepSeek, Google and others.
It isn't a single AI tool so much as infrastructure. Developers use it to find and download models, version their own models and datasets with Git-style repositories, try demos, and read papers through Daily Papers. There's also HuggingChat, a chat interface for open models.
Paid products
Browsing and downloading public models is free. The paid side, from the official pricing page (checked October 2026):
- PRO account, $9/month: 10× private storage, 2× public storage, $2 of inference credits each month, 8× ZeroGPU quota with top queue priority, and the Dataset Viewer on private datasets.
- Team, $20 per user/month: SSO (SAML and OIDC), storage regions for data location, audit logs, resource groups for access control and usage analytics. All members get PRO benefits for ZeroGPU and Inference Providers.
- Enterprise, from $50 per user/month: the highest storage, bandwidth and API rate limits, plus enterprise support.
Compute is billed separately. Spaces can run on free CPU hardware or on upgraded GPUs billed by the hour, and Inference Endpoints, which deploy a model on dedicated autoscaling infrastructure, start at $0.033 an hour. Inference Providers offer API access to models served by partner providers. Hugging Face also offers community GPU grants for side projects.
Who needs it
ML engineers, researchers and developers building on open models. If you just want a chatbot, this isn't the place to start. If you want to compare open models, fine-tune one or ship a demo, it's close to unavoidable.
Pros: huge free catalogue of models and datasets; low-cost PRO tier; pay-as-you-go GPUs without a cloud contract.
Cons: model quality and licences vary from repo to repo, so read each model card; costs for endpoints and GPUs add up if left running; the sheer volume can be overwhelming for beginners.
Alternatives
Google's Gemma models are one of many open families you'll find on the Hub. If you'd rather call a hosted model through a simple API, look at DeepSeek.
Details
- Pricing
- Freemium
- Release date
- 2026-08-17
- Category
- ML
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Pros and cons
Pros
- huge free catalogue of models and datasets
- low-cost PRO tier
- pay-as-you-go GPUs without a cloud contract
Cons
- model quality and licences vary from repo to repo, so read each model card
- costs for endpoints and GPUs add up if left running
- the sheer volume can be overwhelming for beginners
Alternatives
GemmaGemma is Google DeepMind's family of open-weight models, from phone-sized E2B and E4B up to 31B, plus variants for embeddings, translation and health.
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