Hugging Face
Deploy and share ML models via Spaces, ZeroGPU, and API
Editor's take
Worth trying if your team needs a low-friction way to publish model demos and get an API endpoint quickly; best suited for ML engineers who can invest a little time configuring org policies and quotas.
Pros and cons
What works
- Intuitive web UX and Dev Mode reduce onboarding time for getting a hosted demo from a repo.
- Robust API surface (Spaces + Inference API) enables programmatic integration and direct inference calls from apps.
- Controls for private datasets and Storage Regions plus a Dataset Viewer let teams preview data securely and choose data location.
What to watch
- Users report pricing transparency can be unclear at larger scale, making long-term cost planning harder.
- Occasional performance degradation has been reported during traffic peaks, which can affect latency-sensitive apps.
- Advanced features (fine-grained quotas, ZeroGPU queues, organization policies) require a learning curve to configure optimally.
Try It Like This
- 1 Deploy a model repo to Spaces
Create a Hugging Face account → push your model or app repo to a new Space (Git-based) → Dev Mode builds a hosted demo and returns a shareable URL and endpoint for quick testing.
- 2 Expose model via Inference API
Enable the Inference API for your deployed Space or model → obtain an API token in your account settings → make a first API call (curl/SDK) to get inference responses and verify latency and output format.
- 3 Enforce org access and SSO
Create a Team/Organization in Hugging Face → configure SSO and assign repo-level roles → invite members so they can push, review, or deploy under centralized access control.
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- 4 Host private datasets with Dataset Viewer
Upload a private dataset and select a Storage Region → open the Dataset Viewer to preview examples securely → share viewer access with collaborators before publishing or exporting data.
- 5 Run queued jobs on ZeroGPU
Submit batch inference jobs to a Space and select the ZeroGPU queue option → monitor job queue and priority in the dashboard → leverage included inference credits to reduce initial hosting costs and cut wait times during peaks.
Key Features
- Push a repo to Spaces → a hosted app and demo appear with Dev Mode
- Upgrade to PRO → 10× private storage and 2× public storage for models and datasets
- Run inference workloads → 20× included inference credits reduce initial hosting costs
- Queue jobs on ZeroGPU → 8× quota and highest priority cut wait times during peaks
- Create private datasets → preview data securely with the Dataset Viewer and choose Storage Regions for data location control
Use Cases
- An ML engineer hosting a fine-tuned model in Spaces and exposing it via the Inference API for a pilot app
- A data platform team enforcing SSO and Storage Regions while granting granular repo access across a 10-person org
- A researcher sharing a private dataset with collaborators using the Dataset Viewer before publishing results
Pricing
| Plan | Price | Includes |
|---|---|---|
| PRO | $9 per month | 10× private storage, 2× public storage, 20× included inference credits, 8× ZeroGPU quota & highest queue priority, Spaces Dev Mode & ZeroGPU Spaces hosting, Personal blog publishing, Dataset Viewer for private datasets, PRO badge |
| Team | $20 per user per month | SSO (SAML & OIDC), Storage Regions, Audit Logs, Resource Groups, Repository Analytics, Advanced auth and visibility controls, Centralized token control, Dataset Viewer for private datasets, Advanced compute for Spaces, ZeroGPU & Inference Providers PRO benefits |
| Enterprise | Starting at $50 per user per month | All Team benefits, highest storage/bandwidth/API limits, SCIM provisioning, advanced security/access controls, managed billing with annual commitments, legal/compliance processes, dedicated support |
Getting Started
- 1 Create an account on huggingface.co and choose a plan (PRO, Team, or Enterprise).
- 2 Create a Space from a template or repo, or upload a model/dataset to your org.
- 3 Open the Space URL and run a test inference via the API to confirm your deployment works.
About
Deploy a model or app to Spaces and get a hosted endpoint and shareable demo without managing infrastructure. Teams use it to serve models, control data location with Storage Regions, and manage org access via SSO. Differentiators include ZeroGPU queues, a Dataset Viewer for private datasets, and clear storage and inference quotas.
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FAQ
Is Hugging Face free?
It is a paid service.
What platforms is Hugging Face available on?
Available on Web, API.
Does Hugging Face support Korean?
Korean is not currently supported.