--- title: "ai-getting-started vs aikit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/a16z-infra-ai-getting-started-vs-kaito-project-aikit" tools: ["a16z-infra-ai-getting-started", "kaito-project-aikit"] --- # ai-getting-started vs aikit *GraphCanon updated Aug 24, 2026* ## Verdict Pick ai-getting-started if ai-getting-started is a TypeScript-based JavaScript AI tool tailored for weekend projects, offering everything from model implementation to deployment configurations; 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. [ai-getting-started](https://ai-getting-started.com/) reports 4.1k GitHub stars, 660 forks, and 16 open issues, last pushed Aug 21, 2024. [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 [ai-getting-started's repository](https://github.com/a16z-infra/ai-getting-started) and [aikit's repository](https://github.com/kaito-project/aikit). | | [ai-getting-started](/tools/a16z-infra-ai-getting-started.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Tagline | A Javascript AI getting started stack for weekend projects | Fine-tune, build, and deploy open-source LLMs easily! | | Stars | 4,141 | 537 | | Forks | 660 | 57 | | Open issues | 16 | 40 | | Language | TypeScript | Go | | Adopt for | ai-getting-started is a TypeScript-based JavaScript AI tool tailored for weekend projects, offering everything from model implementation to deployment configurations. | 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 | Developer Tools, Model Training, Vector Databases | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-getting-started](/tools/a16z-infra-ai-getting-started.md) | [aikit](/tools/kaito-project-aikit.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 723d | 0d | | Open issues (now) | 16 | 40 | | Stars delta | 0 (30d) | +3 (30d) | | Open issues delta | 0 (30d) | -3 (30d) | | Full report | [trust report](/tools/a16z-infra-ai-getting-started/trust.md) | [trust report](/tools/kaito-project-aikit/trust.md) | ## Decision facts: ai-getting-started - **Adopt for:** ai-getting-started is a TypeScript-based JavaScript AI tool tailored for weekend projects, offering everything from model implementation to deployment configurations. ## 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 ai-getting-started if… - ai-getting-started is primarily TypeScript; aikit is Go. - Tags unique to ai-getting-started: deployment, image models, javascript, text models. - Also covers Developer Tools, Vector Databases. - * Use this tool if you are already familiar with or prefer working in TypeScript and want an easy entry point into AI project development. ### Choose aikit if… - aikit is primarily Go; ai-getting-started is TypeScript. - Tags unique to aikit: ai, buildkit, chatgpt, docker. - Also covers Inference & Serving, LLM Frameworks. - - You need a flexible solution specifically built using Go and prefer its concurrency model. ## When NOT to use ai-getting-started - * If your focus is on developing large-scale, production-level applications, this tool may not offer the necessary scalability features. - * Not suitable if you require highly customized vector stores or specific AI model training environments beyond what the package provides as it focuses more on a general setup. ## 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 ai-getting-started and aikit? ai-getting-started: A Javascript AI getting started stack for weekend projects. 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 ai-getting-started over aikit? Choose ai-getting-started over aikit when ai-getting-started is primarily TypeScript; aikit is Go; Tags unique to ai-getting-started: deployment, image models, javascript, text models; Also covers Developer Tools, Vector Databases; * Use this tool if you are already familiar with or prefer working in TypeScript and want an easy entry point into AI project development. ### When should I choose aikit over ai-getting-started? Choose aikit over ai-getting-started when aikit is primarily Go; ai-getting-started is TypeScript; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers Inference & Serving, LLM Frameworks; - You need a flexible solution specifically built using Go and prefer its concurrency model. ### When should I avoid ai-getting-started? * If your focus is on developing large-scale, production-level applications, this tool may not offer the necessary scalability features. * Not suitable if you require highly customized vector stores or specific AI model training environments beyond what the package provides as it focuses more on a general setup. ### 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 ai-getting-started or aikit more popular on GitHub? ai-getting-started has more GitHub stars (4,141 vs 537). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-getting-started and aikit open source? Yes - both are open-source projects on GitHub (ai-getting-started: MIT, aikit: MIT). ### Where can I find alternatives to ai-getting-started or aikit? GraphCanon lists graph-backed alternatives at [ai-getting-started alternatives](/tools/a16z-infra-ai-getting-started/alternatives) and [aikit alternatives](/tools/kaito-project-aikit/alternatives) ([ai-getting-started markdown twin](/tools/a16z-infra-ai-getting-started/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/a16z-infra-ai-getting-started-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, ai-getting-started or aikit? ai-getting-started: Dormant. 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 ai-getting-started and aikit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-getting-started trust report](/tools/a16z-infra-ai-getting-started/trust); [aikit trust report](/tools/kaito-project-aikit/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=a16z-infra-ai-getting-started`](/api/graphcanon/graph?tool=a16z-infra-ai-getting-started) - 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/_