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Eligibility Criteria
- Must be a FounderPass member
- New or eligible Hugging Face Pro user
- Must redeem through the official FounderPass link
All approvals and account eligibility remain subject to Hugging Face’s terms.
How to redeem this perk
- Log in to your FounderPass account.
- Click through to the Hugging Face offer page.
- Activate the 2 month free Pro offer.
The free period will apply according to Hugging Face’s billing terms.
About Hugging Face
Hugging Face is the platform powering a large share of today’s open AI ecosystem. It hosts thousands of models, datasets, and demo applications used by startups, research teams, and enterprise companies worldwide.
For founders building AI native products, Hugging Face often becomes the central hub for experimentation and deployment. Teams can discover pre trained models, fine tune them, version control their work, and deploy inference endpoints, all within one ecosystem.
The platform is particularly powerful for startups that want to ship AI features quickly without building everything from scratch. Instead of training foundational models in house, teams can build on proven open models and focus on product differentiation.
Why startups and founders use Hugging Face
Access to State of the Art Models
Hugging Face hosts many of the world’s most widely used open source LLMs, vision models, and speech models. This dramatically shortens development cycles.
Private Model and Dataset Hosting
With Pro, teams can host private models and datasets securely, making it suitable for early stage commercial products.
Collaboration and Community
Hugging Face is more than a hosting platform. It is a global AI community. Engineers can collaborate, fork models, track experiments, and share improvements in a structured way.
Production Ready Deployment
Startups can move from experimentation to production using hosted inference endpoints and scalable infrastructure, avoiding the complexity of self managing model serving from day one.
Who This Is Best For
- AI first startups
- SaaS products embedding LLM features
- Teams building NLP, computer vision, or speech applications
- Founders experimenting with open source models before raising significant funding
If your product roadmap includes AI, Hugging Face Pro gives you structured infrastructure and community support without immediate cost.
What is included in Hugging Face Pro?
Hugging Face Pro typically includes enhanced platform features such as private model and dataset hosting, increased compute limits, priority access to certain resources, and improved collaboration capabilities.
Exact features are determined by Hugging Face’s current Pro plan structure and may evolve over time.
Who is this offer best suited for?
This offer is ideal for AI focused startups, technical founders, and product teams building with machine learning models.
If you are experimenting with LLMs, vision models, speech models, or custom fine tuning workflows, Hugging Face Pro can give you more flexibility and control compared to a standard free account.
Do I need to be an AI company to use Hugging Face?
No. Many SaaS startups embed AI features without being pure AI companies.
If you are adding features such as text generation, classification, summarisation, image analysis, or recommendation systems, Hugging Face can help you prototype and deploy these capabilities efficiently.
What happens after the 2 month free period ends?
After the 2 month free period, your Hugging Face Pro subscription will continue at standard pricing unless you cancel.
We recommend reviewing Hugging Face’s current pricing before activating the offer so you understand ongoing costs.
Can I use this offer if I already have a Hugging Face account?
In most cases, the offer is available to new Pro upgrades rather than existing paid Pro subscribers.
If you already have an account, you may still be eligible to upgrade through the FounderPass link, but eligibility is subject to Hugging Face’s terms.
Is this suitable for production use?
Yes. Many startups use Hugging Face in production, particularly for hosting models, managing versions, and deploying inference endpoints.
However, production readiness depends on your architecture, security requirements, and scaling needs. Founders building customer facing AI features should ensure their infrastructure design aligns with their performance and compliance requirements.
Deal change log
- 12th Feb, 2026 - New deal added