fast.ai pricing, free plan and limits

Last updated: 2026-08-19

fast.ai's free Practical Deep Learning for Coders course has taught over 500,000 students worldwide using a top-down, code-first method that trains a working model before explaining the theory behind it. It ships alongside fastai, an open-source PyTorch add-on library, and forums.fast.ai for peer support.

About fast.ai

fast.ai is a nonprofit research organization founded in 2016 by Jeremy Howard and Rachel Thomas. Its Practical Deep Learning for Coders course teaches computer vision, NLP, and recommendation systems with a top-down approach: working implementations come first, theory follows. The only prerequisites are about a year of Python coding experience and high-school-level math. The course runs seven weeks and moves through image classification, CNNs, transformers, time series, generative models, recommendation systems, and deployment, with every lesson delivered as video, transcript, and an executable Jupyter notebook. fast.ai also maintains the open-source fastai library, a high-level API sitting on top of PyTorch, and forums.fast.ai, where students give each other peer support and mentoring. Many fast.ai graduates have gone on to win international machine learning competitions and land roles at top AI companies, a track record the course cites as evidence its practical-first method works even without a formal AI degree. fast.ai publishes the companion Deep Learning for Coders book through O'Reilly and translates course content into multiple languages, extending access beyond English-speaking learners. The organization's nonprofit structure means it runs on founder resources and grants rather than tuition or certificate fees.

Pricing

fast.ai is entirely free: the Practical Deep Learning for Coders course, all video lessons, the fastai library, and the Deep Learning for Coders book carry no charge. There are no paid tiers, seat limits, or enrollment fees; the nonprofit runs on founder resources and grant funding instead of course fees.

Key Features

• Top-Down, Model-First Curriculum: Each lesson opens with a working model students can run immediately, then unpacks the underlying math and architecture in the following lesson rather than teaching theory upfront.
• fastai Library on PyTorch: Open-source (Apache 2.0) deep learning library that sits on top of PyTorch and cuts typical training code by about 95%, using high-level APIs and pre-tuned model architectures maintained on GitHub.
• Peer Support Forums: forums.fast.ai gives students direct access to prior graduates and current classmates for debugging code and discussing lessons, the course's main channel for help since there is no live instructor Q&A.
• Deep Learning for Coders Book: O'Reilly-published book by the same course authors, following the identical top-down teaching order chapter by chapter, and available to read online at no cost.
• Multilingual Course Translations: Volunteer translators have ported the video lessons and written materials into multiple languages, extending the free course to non-English-speaking learners worldwide.

Pros

• Every course, the fastai library, and the companion book stay free indefinitely, since fast.ai runs as a nonprofit rather than a subscription business.
• The top-down, model-first structure lets working professionals see a result in lesson one instead of sitting through weeks of theory before writing code.
• The fastai library's high-level API cuts the code needed for a typical PyTorch training loop dramatically, so students spend more time on modeling decisions and less on repetitive setup code.

Cons

• The top-down order can frustrate learners who want the underlying math and theory explained before they see it applied; picking up supplementary papers for that depth is left to the student.
• There is no live instructor conducting the course: help comes only through forums.fast.ai, so response speed depends on who else happens to be online.

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