# fast.ai vs Models.dev

fast.ai records 250,720 monthly visits with -10.4% month-over-month growth and Free pricing. Models.dev records 200,451 monthly visits with -3.9% growth and Free pricing.

| Metric | [fast.ai](https://openaitools.app/tool/fast-ai) | [Models.dev](https://openaitools.app/tool/models-dev) |
| :--- | :--- | :--- |
| Website URL | https://www.fast.ai | https://models.dev |
| Categories | AI Course, Large Language Models (LLMs), AI Developer Tools, AI Research Tool | AI Models, AI API, Open Source AI Models, AI Tools Directory |
| Pricing Model | Free | Free |
| Monthly Visits | 250,720 | 200,451 |
| MoM Growth Rate | -10.4% | -3.9% |
| Primary Market | United States | China |
| Direct Visits | 136,995 | 136,316 |
| Review Score | 4.8 | 4.4 |
| Saved Count | 4 | 0 |
| Supported Platforms | Website | Website |

## fast.ai

fast.ai began with a simple premise: deep learning should be taught top-down, starting with working code that produces results on day one, and only then unpacking the theory. That approach produced Practical Deep Learning for Coders, one of the most widely followed free courses in the field, aimed at people with programming experience rather than advanced mathematics. Later offerings include From Deep Learning Foundations to Stable Diffusion and the newer How to Solve It With Code, which replaced the traditional course format with a problem-solving platform.

Software is the second pillar. The fastai library layers a high-level, opinionated API on top of PyTorch so that training state-of-the-art models takes a handful of lines, while remaining flexible enough for research. nbdev lets teams develop, document and publish entire Python packages from inside Jupyter notebooks, solving the long-standing friction between notebooks and version control. Both projects are open source and supported by an active community forum and documentation.

Alongside teaching and tooling, fast.ai publishes widely read writing on the societal side of AI, including critiques of how AI power concentrates, debates about AI safety framing, and analysis of education and technology. In late 2024 the organisation announced it was joining Answer.AI, a new kind of R&D lab focused on building practical end-user products from foundational research, while the fast.ai blog and course materials remain freely available.

**Pricing & Plans:** Everything fast.ai produces is free: the courses, the fastai and nbdev libraries, the documentation and the blog. There is no paid tier, though donations and community contributions are welcome.

- **Practical Deep Learning for Coders:** A free, top-down course that gets you training real models immediately and explains the theory as you go, with notebooks and video lessons.
- **How to Solve It With Code:** A newer educational format that teaches iterative, exploration-driven problem solving with an interactive platform, replacing the classic course structure.
- **fastai library for PyTorch:** A high-level API that makes modern deep learning techniques concise and accessible while staying flexible for research work.
- **nbdev:** Develop, test, document and publish complete Python packages directly from Jupyter notebooks, ending the notebook-versus-git conflict.
- **Research and blog:** Long-form articles on AI in society, education, ethics, technical deep dives and attitudes, plus an active community forum.

**Strengths:** Completely free courses, libraries and reading material with no paywall; Top-down teaching style that suits working developers; Backed by production-grade open-source software used well beyond the courses; Independent, critical writing on AI ethics and society alongside technical content

**Limitations:** Teaching style is opinionated, so some learners prefer a theory-first curriculum; Course content evolves gradually and older materials can drift out of date; No formal certification, tutoring or career support is offered

[Profile (Markdown)](https://openaitools.app/tool/fast-ai.md)

## Models.dev

Models.dev exists to answer a very practical developer question: which model should I use, and what will it cost? The site organises every entry under a canonical identifier in the form provider/model, so the same model served by several providers can be compared directly rather than guessed at. Columns cover context window, maximum output, whether input and output are supported, reasoning, tool calling, structured output, temperature control, open versus closed weights, price per million tokens, and release and update dates.

Because model releases move fast, the catalogue is built to stay current, with sortable and searchable tables that let you scan by lab or provider and filter down to what matters. Entries include both closed flagship models and open-weight alternatives, making it useful for cost planning as well as capability research.

The project is open source and free to use, and it is intended to be consumed programmatically. Developers can pull the catalogue into their own tooling through a JSON API or a type-safe SDK, which makes it a practical backend for dashboards, routing logic, or internal cost calculators rather than just a reference page to browse.

**Pricing & Plans:** Models.dev is free and open source. It is a reference database, so the prices it lists are the per-million-token rates charged by the model providers themselves, not a fee for using the site.

- **Canonical model identifiers:** Every model uses a consistent provider/model format so the same model across providers can be compared directly.
- **Specification comparison:** Side-by-side context window, output limit, reasoning, tool calling, structured output, and temperature support.
- **Pricing transparency:** Input and output prices per million tokens listed alongside release and update dates.
- **Open and closed models:** Covers closed flagship APIs and open-weight models from many labs in one sortable catalogue.
- **Developer access:** Consume the catalogue through a JSON API or a type-safe SDK for your own tools and dashboards.

**Strengths:** Free and open source with no account required; Consistent canonical identifiers make cross-provider comparison reliable; Covers pricing, limits, and capabilities in one sortable table; Available as a JSON API and type-safe SDK for programmatic use

**Limitations:** It is a reference catalogue, not an inference or hosting service; Data quality depends on community and maintainer updates keeping pace with releases; Interface is aimed at developers and offers little guidance for non-technical users

[Profile (Markdown)](https://openaitools.app/tool/models-dev.md)

## Frequently Asked Questions

### How do fast.ai and Models.dev compare in monthly traffic?

fast.ai has 250,720 monthly visits, while Models.dev has 200,451. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

fast.ai has a month-over-month growth rate of -10.4%; Models.dev has -3.9%.

### How does pricing compare?

fast.ai: Free. Everything fast.ai produces is free: the courses, the fastai and nbdev libraries, the documentation and the blog. There is no paid tier, though donations and community contributions are welcome. Models.dev: Free. Models.dev is free and open source. It is a reference database, so the prices it lists are the per-million-token rates charged by the model providers themselves, not a fee for using the site.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [DeepLearning.AI](https://openaitools.app/tool/deeplearning-ai) | 2,562,512 | -7.2% | Freemium | Offers broad structured courses by Andrew Ng, but lacks fast.ai's deep hands-on focus on PyTorch code. | [.md](https://openaitools.app/tool/deeplearning-ai.md) |
| 2 | [OpenAI Academy](https://openaitools.app/tool/openai-academy) | 937,379 | -6.3% | Free | Provides official structured learning resources for AI development, but centers on OpenAI's proprietary ecosystem rather than PyTorch. | [.md](https://openaitools.app/tool/openai-academy.md) |
| 3 | [LabLab.ai](https://openaitools.app/tool/lablab-ai) | 431,529 | -39.7% | Free | Hosts hands-on AI hackathons and developer tutorials, but emphasizes rapid prototyping over deep academic machine learning. | [.md](https://openaitools.app/tool/lablab-ai.md) |
| 4 | [Stork](https://openaitools.app/tool/stork) | 201,544 | +2.3% | Free | Includes model pricing trackers and developer news alongside tools directories, but less focused on raw API specs. | [.md](https://openaitools.app/tool/stork.md) |
| 5 | [Learn Prompting](https://openaitools.app/tool/learn-prompting) | 159,807 | -8.6% | Freemium, Free Trial, Contact for Pricing | Offers free educational guides on prompt engineering, but focuses on LLM usage rather than training neural networks. | [.md](https://openaitools.app/tool/learn-prompting.md) |
| 6 | [Open Data Science (ODS.ai)](https://openaitools.app/tool/ods-ai) | 159,630 | +15.9% | Free | A vibrant open community for data science practitioners, though it operates as a network rather than structured courses. | [.md](https://openaitools.app/tool/ods-ai.md) |
| 7 | [The Full Stack (fullstackdeeplearning.com)](https://openaitools.app/tool/fullstackdeeplearning-com) | 46,082 | +6.6% | Free (educational content) | Provides practical production machine learning tutorials, but covers broader deployment stacks rather than pure deep learning foundations. | [.md](https://openaitools.app/tool/fullstackdeeplearning-com.md) |
| 8 | [InfraBase.ai](https://openaitools.app/tool/infrabase-ai) | 39,072 | -13.6% | Free | Catalogues and compares AI infrastructure tools and services across categories, but lacks deep per-token pricing tables. | [.md](https://openaitools.app/tool/infrabase-ai.md) |
| 9 | [LAION](https://openaitools.app/tool/laion) | 18,486 | -24.1% | Free | Publishes open-source datasets and research models, but focuses on raw AI research rather than structured educational tutorials. | [.md](https://openaitools.app/tool/laion.md) |
| 10 | [Build Club](https://openaitools.app/tool/build-club) | 11,166 | -53.9% | Free | Learn AI by building, with a global community of makers | [.md](https://openaitools.app/tool/build-club.md) |

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Source: https://openaitools.app/compare/fast-ai-vs-models-dev

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