What it is
Hugging Face is where most open-weight AI models and datasets are published. On top of the free hub it sells Spaces (hosting for AI demo apps), Inference Providers (one OpenAI-compatible API that routes to partner hosts such as Groq, Together AI and Cerebras at their own prices) and Inference Endpoints (dedicated GPUs that run a model just for you).
Accounts are free, with 100GB of private storage but no included inference credit. PRO is $9 a month and adds $2 of monthly compute credit, 1TB of private storage and 8x the ZeroGPU quota. Team is $20 per user a month and Enterprise $50, each seat adding $2 of pooled credit and 1TB of private storage. On 3 September 2026 NVIDIA announced it had agreed to buy Hugging Face for about $12.93 billion; the announcement gives no closing date.
Our review
Hugging Face matters because nearly everything open lands there first. NVIDIA's acquisition announcement puts the hub at more than 3 million models, 500,000 datasets and 1 million apps, used by over 18 million developers and 200,000 companies. For an engineer it is plumbing: transformers, vLLM, llama.cpp and Ollama all pull weights from its file URLs, which is why the setting that matters most is an access token. Anonymous downloads are capped at 3,000 file requests per five minutes per IP address, against 5,000 for a signed-in free user and 12,000 on PRO.
The paid layer is three products of uneven value. Inference Providers puts one OpenAI-compatible endpoint in front of more than 200 models on partner hosts, at the partners' own rates with no markup; it is the cheapest way to compare providers, but free accounts no longer get any included credit, so testing needs a card or PRO's $2 a month. Inference Endpoints rents dedicated GPUs: an H100 is $4.50 an hour and an H200 $5.00, which undercuts Together AI's listed $5.49 for a dedicated H100, though not its $3.99 cluster rate. Spaces remains the quickest way to put a demo online, with free shared ZeroGPU time.
Two details catch teams out. Text Generation Inference, Hugging Face's own model server, is in maintenance mode, and its docs now send new deployments to vLLM or SGLang. And private storage is metered: 100GB free, 1TB on PRO or per paid seat, then $18 per TB a month, which adds up for a team pushing a checkpoint from every training run.
The NVIDIA deal is the strategic question. Jensen Huang's post promises that the platform stays open and that NVIDIA compute will not be required to build on or deploy through it, but it gives no closing date, and TechCrunch points to the commercial logic of bundling NVIDIA's spare capacity with Hugging Face services. The same report notes that OpenAI said in July its unreleased models had breached Hugging Face. The hub scans uploads for malware, unsafe pickle files and leaked secrets, and the company is SOC 2 Type 2 certified, but for a procurement team its neutrality is now a promise rather than a structure.
Where it earns its place
An ML engineer shortlisting three open models for a support chatbot
Compare quality and latency across hosts before committing to one
Point the OpenAI SDK at router.huggingface.co and switch model and provider by name, paying the providers' own per-token prices; PRO's $2 of monthly credit covers light testing, after which usage bills to a card.
A 15-person university research lab
Keep private checkpoints and datasets with access control
Team at $20 a seat costs $300 a month and includes 15TB of private storage (1TB per seat), SSO, audit logs and storage regions; anything above that bills at $18 per TB a month.
A platform engineer serving a fine-tuned 70B model
Run a private endpoint without operating Kubernetes
Two H100s on Inference Endpoints cost $9.00 an hour, about $6,570 a month if left running for 730 hours, so it pays only with steady traffic; bursty workloads are cheaper through per-token Inference Providers.
A two-founder startup preparing an investor demo
Put a working model demo online this week
A Gradio app on Spaces with free ZeroGPU time costs nothing; PRO at $9 gives 8x the ZeroGPU quota and top queue priority, and a dedicated T4 from $0.40 an hour keeps the demo warm.
Before you commit
- Setup
- Create an account and an access token; models download through transformers or the hf CLI in minutes, and Inference Providers works with the OpenAI SDK by changing the base URL.
- Integrations
- huggingface_hub (Python) and @huggingface/inference (JavaScript) SDKs; weights load in vLLM, SGLang, llama.cpp, MLX and Ollama; Inference Endpoints run on AWS, Azure and Google Cloud.
- Data and privacy
- SOC 2 Type 2 and GDPR; BAAs and data processing agreements on Enterprise; SSO, audit logs and storage regions from Team; SCIM on Enterprise.
- Cost at scale
- A 50-seat Team organisation pays $1,000 a month, with $100 of pooled compute credit and 50TB of private storage; one always-on H100 endpoint adds about $3,285 a month.
- Limits
- Hub requests are rate-limited per five-minute window (free users 1,000 API and 5,000 file requests, PRO 2,500 and 12,000, applied per member in organisations); single files are capped at 500GB.
What's good
- Largest public library of open models and datasets
- Inference Providers passes through partner prices with no markup
- Dedicated H100 at $4.50/hour, below Together AI's dedicated rate
- Team plan at $20/user adds SSO, audit logs and storage regions
- SOC 2 Type 2; malware, pickle and secrets scanning on uploads
What's not
- Free accounts get no included inference credit
- TGI is in maintenance mode; new deployments should use vLLM or SGLang
- Private storage beyond the included 1TB costs $18/TB a month
- Pending NVIDIA ownership leaves its neutrality resting on a promise
Best for
Engineers and research teams who find, share, test or fine-tune open models, and organisations that need a private model registry.
Alternatives
Hugging Face: common questions
- Is Hugging Face worth it?
- Indispensable as a model hub, competitive but unremarkable as a host, and soon to be owned by the largest seller of AI chips. We rate it 4.7 out of 5.
- How much does Hugging Face cost?
- Free account (100GB private storage, no included inference credit); PRO $9/month ($2 compute credit); Team $20/user/month; Enterprise $50/user/month. Spaces GPUs from $0.40/hour; Inference Endpoints from $0.03/hour (CPU), H100 $4.50/hour.
- What are the downsides of Hugging Face?
- Free accounts get no included inference credit; tGI is in maintenance mode; new deployments should use vLLM or SGLang; private storage beyond the included 1TB costs $18/TB a month; pending NVIDIA ownership leaves its neutrality resting on a promise.
- Who is Hugging Face best for?
- Engineers and research teams who find, share, test or fine-tune open models, and organisations that need a private model registry.
- How much does Hugging Face cost for a team or in production?
- A 50-seat Team organisation pays $1,000 a month, with $100 of pooled compute credit and 50TB of private storage; one always-on H100 endpoint adds about $3,285 a month.
- Is Hugging Face safe for company data?
- SOC 2 Type 2 and GDPR; BAAs and data processing agreements on Enterprise; SSO, audit logs and storage regions from Team; SCIM on Enterprise.
- What are the best Hugging Face alternatives?
- The closest alternatives we have reviewed are Replicate, Together AI, Modal and OpenRouter.
Answers checked Oct 22, 2026.
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