--- title: "scaling-book vs aikit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jax-ml-scaling-book-vs-kaito-project-aikit" tools: ["jax-ml-scaling-book", "kaito-project-aikit"] --- # scaling-book vs aikit *GraphCanon updated Aug 25, 2026* ## Verdict Pick scaling-book if scaling-book; pick aikit if aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. [scaling-book](https://jax-ml.github.io/scaling-book/) reports 1.4k GitHub stars, 191 forks, and 8 open issues, last pushed Aug 20, 2026. [aikit](https://kaito-project.github.io/aikit/) has 537 stars, 57 forks, and 40 open issues, last pushed Aug 24, 2026. Figures are from public GitHub metadata via [scaling-book's repository](https://github.com/jax-ml/scaling-book) and [aikit's repository](https://github.com/kaito-project/aikit). | | [scaling-book](/tools/jax-ml-scaling-book.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Tagline | Guide on scaling LLMs on TPUs | Fine-tune, build, and deploy open-source LLMs easily! | | Stars | 1,368 | 537 | | Forks | 191 | 57 | | Open issues | 8 | 40 | | Language | HTML | Go | | Adopt for | scaling-book | Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [scaling-book](/tools/jax-ml-scaling-book.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Days since push | 4d | 0d | | Open issues (now) | 8 | 40 | | Stars delta | +77 (30d) | +3 (30d) | | Open issues delta | +1 (30d) | -3 (30d) | | Full report | [trust report](/tools/jax-ml-scaling-book/trust.md) | [trust report](/tools/kaito-project-aikit/trust.md) | ## Decision facts: scaling-book - **Adopt for:** scaling-book ## Decision facts: aikit - **Adopt for:** Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. ## Choose when ### Choose scaling-book if… - scaling-book is primarily HTML; aikit is Go. - Tags unique to scaling-book: jax, llm-inference, llms, roofline. - You are working specifically with machine learning models that leverage Tensor Processing Units (TPUs) for performance and are looking to understand optimization techniques. ### Choose aikit if… - aikit is primarily Go; scaling-book is HTML. - Tags unique to aikit: ai, buildkit, chatgpt, docker. - Also covers Model Training. - aikit ships Docker support for self-hosted deployment. - - You need a flexible solution specifically built using Go and prefer its concurrency model. ## When NOT to use scaling-book - Do not use if your project focuses exclusively on GPU scaling or other hardware not aligned with Tensor Processing Units (TPUs). - If you are looking for a general approach to any framework's scalability without emphasis on TPUs. - This resource is unsuitable if you need information about model training phases, as it emphasizes inference and serving phases. ## When NOT to use aikit - - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these. ## Common questions ### What is the difference between scaling-book and aikit? scaling-book: Guide on scaling LLMs on TPUs. aikit: Fine-tune, build, and deploy open-source LLMs easily!. See the comparison table for live GitHub stats and shared categories. ### When should I choose scaling-book over aikit? Choose scaling-book over aikit when scaling-book is primarily HTML; aikit is Go; Tags unique to scaling-book: jax, llm-inference, llms, roofline; You are working specifically with machine learning models that leverage Tensor Processing Units (TPUs) for performance and are looking to understand optimization techniques. ### When should I choose aikit over scaling-book? Choose aikit over scaling-book when aikit is primarily Go; scaling-book is HTML; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers Model Training; aikit ships Docker support for self-hosted deployment; - You need a flexible solution specifically built using Go and prefer its concurrency model. ### When should I avoid scaling-book? Do not use if your project focuses exclusively on GPU scaling or other hardware not aligned with Tensor Processing Units (TPUs). If you are looking for a general approach to any framework's scalability without emphasis on TPUs. This resource is unsuitable if you need information about model training phases, as it emphasizes inference and serving phases. ### When should I avoid aikit? - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these. ### Is scaling-book or aikit more popular on GitHub? scaling-book has more GitHub stars (1,368 vs 537). Stars measure visibility, not whether either tool fits your constraints. ### Are scaling-book and aikit open source? Yes - both are open-source projects on GitHub (scaling-book: MIT, aikit: MIT). ### Where can I find alternatives to scaling-book or aikit? GraphCanon lists graph-backed alternatives at [scaling-book alternatives](/tools/jax-ml-scaling-book/alternatives) and [aikit alternatives](/tools/kaito-project-aikit/alternatives) ([scaling-book markdown twin](/tools/jax-ml-scaling-book/alternatives.md), [aikit markdown twin](/tools/kaito-project-aikit/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/jax-ml-scaling-book-vs-kaito-project-aikit.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, scaling-book or aikit? scaling-book: Very active. aikit: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for scaling-book and aikit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [scaling-book trust report](/tools/jax-ml-scaling-book/trust); [aikit trust report](/tools/kaito-project-aikit/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jax-ml-scaling-book`](/api/graphcanon/graph?tool=jax-ml-scaling-book) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_