Hugging Face

The open hub for machine learning models, datasets, and demos.

Hand-reviewed by the Toolglade team·Updated Aug 2026

coding#open-source#machine-learning#model-hub#inference

Free plan Claimed API Teams

Toolglade’s take

Hugging Face is close to essential if you work with open models: the Hub, libraries, and community are unmatched in breadth. The free tier covers a lot, and Pro ($9/mo) plus Enterprise Hub add limits and governance. The main caveat is sprawl. Between the Hub, Spaces, Inference Endpoints, and Inference Providers there are several overlapping ways to run a model, and pricing depends heavily on which path and which GPU you pick, so budget carefully for production inference.

About Hugging Face

Hugging Face is the central hub and toolchain for open machine learning. It hosts models, datasets, and demos, maintains widely used open-source libraries, and sells managed inference, collaboration, and enterprise governance on top. Most of the public Hub is free; paid tiers add higher limits, private infrastructure, and security controls.

Hugging Face began as the maintainer of the open-source Transformers library and grew into the de facto home for open machine learning. The Hub hosts millions of models, datasets, and Spaces (hosted demos), and its libraries (Transformers, Diffusers, Datasets, Accelerate) are standard tooling across research and production ML. Beyond hosting, Hugging Face offers paid services: Inference Endpoints for deploying models on managed GPUs, Inference Providers that route requests to third-party serverless backends, an Enterprise Hub with security and governance controls, and a Pro subscription with higher limits. Much of the core Hub remains free for public repositories. It is best understood as infrastructure plus community rather than a single product. Individual developers use it to find and prototype models; teams use it for private model registries, collaboration, and managed deployment. The breadth is a strength and a caveat: the surface area is large, and navigating which paid service fits a given workload takes some familiarity.

TL;DR

Hugging Face is the central hub for open machine learning, hosting millions of models, datasets, and demos, plus the standard Transformers toolchain. Most of the public Hub is free; paid tiers add higher limits, private infrastructure, and enterprise governance. It is close to essential for anyone building with open models. The main downsides are a sprawling product surface and inference costs that scale with GPU choice. It suits ML engineers, researchers, and teams more than non-technical users.

Company overview

Hugging Face was founded in 2016 by Clement Delangue, Julien Chaumond, and Thomas Wolf. It started as a chatbot app, pivoted to open-source NLP tooling, and became known for the Transformers library, which grew into a broad ecosystem for open machine learning.

The company is headquartered in New York. Its CEO, Clement Delangue, reported in 2026 that annual recurring revenue had surpassed $100 million, and the company has been widely reported to be exploring strategic options at a high valuation.

Product features

The Hub hosts models, datasets, and Spaces (hosted demos). Open-source libraries including Transformers, Diffusers, Datasets, and Accelerate provide the tooling to train, fine-tune, and run models.

Paid services include Inference Endpoints for managed GPU deployment, Inference Providers for serverless routing to third-party backends, a Pro subscription, and an Enterprise Hub with SSO, audit logs, and governance controls for teams.

Target market

Machine-learning engineers, researchers, data scientists, and engineering teams building AI features on open models, from individual developers to large enterprises needing private, governed model management.

Buyer personas

End users

ML engineers, researchers, and data scientists discovering, fine-tuning, and deploying open models.

Buyers

Engineering leaders and platform teams purchasing Enterprise Hub and managed inference.

Key influencers

Open-source maintainers, ML educators, and community contributors.

Ideal customer profile

A technical organization building products on open models that needs a shared, governed hub plus flexible paths to managed inference.

Funding & performance

Hugging Face raised a reported $235 million Series D in 2023 at a roughly $4.5 billion valuation, led by Salesforce with participation from Google, Amazon, Nvidia, and others. A later Series E was reported in 2025 at a significantly higher valuation. In August 2026 it was widely reported to be exploring a sale at a valuation of $13 billion or more; treat the sale reports as unconfirmed.

Pros & cons

Pros

Largest catalog of open models and datasets
Standard-setting open-source libraries
Generous free tier for public work
Strong community and documentation
Multiple deployment paths from prototype to production
Broad framework and cloud integrations
Pro tier is inexpensive at $9/mo

Cons

Large, sometimes confusing product surface
Production inference costs scale with GPU choice and can be unpredictable
Overlapping ways to run models can confuse newcomers
Model quality on the Hub varies widely and is not curated
Enterprise features require a paid plan
Support depth is limited on free tiers

Pricing plans

Free

$0 / month

Unlimited public models, datasets, and Spaces
Access to open-source libraries
Community support
Basic rate limits

Pro

$9 / month

Higher rate limits
More storage
Private repository perks
Early access features

Enterprise Hub

~$20/user / month

SSO and SAML
Audit logs
Access controls and governance
Priority support

Inference Endpoints

Usage-based

Managed GPU deployment
Autoscaling
Billed by compute time
Choice of hardware

Key features

API

Team collaboration

Multi-language

Integrations

Transformers, PyTorch, TensorFlow, LangChain, AWS, Azure, Google Cloud

Input types

text, image, audio, code

Output types

text, image, audio, embeddings

Best For

Finding open models, Sharing ML work, Managed inference, Team model registries

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Frequently asked questions

Is Hugging Face free?+

Yes for most public use. Hosting public models, datasets, and Spaces is free, and the core libraries are open source. Pro ($9/mo), Enterprise Hub, and managed inference are paid.

Can I run models without setting up my own servers?+

Yes. Inference Endpoints deploy a model on managed GPUs, and Inference Providers route requests to serverless backends. Both are usage-based.

Are the models on the Hub reviewed or curated?+

No. Anyone can upload models. Quality, licensing, and safety vary, so you should check model cards and licenses before production use.

Does Hugging Face support private and enterprise use?+

Yes. Private repositories, team collaboration, SSO, and audit logging are available on Pro and Enterprise Hub plans.

Who is behind Hugging Face?+

Hugging Face is an AI company founded in 2016 by Clement Delangue, Julien Chaumond, and Thomas Wolf, headquartered in New York.