--- title: "aikit vs awesome-free-llm-apis" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kaito-project-aikit-vs-mnfst-awesome-free-llm-apis" tools: ["kaito-project-aikit", "mnfst-awesome-free-llm-apis"] --- # aikit vs awesome-free-llm-apis *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick awesome-free-llm-apis if awesome-free-llm-apis curates permanent free Large Language Model APIs with notable details on their limitations and access methods. [aikit](https://kaito-project.github.io/aikit/) reports 539 GitHub stars, 57 forks, and 37 open issues, last pushed Sep 18, 2026. [awesome-free-llm-apis](https://github.com/mnfst/awesome-free-llm-apis) has 7.9k stars, 746 forks, and 17 open issues, last pushed Aug 21, 2026. Figures are from public GitHub metadata via [aikit's repository](https://github.com/kaito-project/aikit) and [awesome-free-llm-apis's repository](https://github.com/mnfst/awesome-free-llm-apis). | | [aikit](/tools/kaito-project-aikit.md) | [awesome-free-llm-apis](/tools/mnfst-awesome-free-llm-apis.md) | | --- | --- | --- | | Tagline | Fine-tune, build, and deploy open-source LLMs easily! | List of Permanent Free LLM API | | Stars | 539 | 7,852 | | Forks | 57 | 746 | | Open issues | 37 | 17 | | Language | Go | JavaScript | | Adopt for | Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. | awesome-free-llm-apis curates permanent free Large Language Model APIs with notable details on their limitations and access methods. | | Persona | - | - | | Runtime | - | - | | License | MIT | This resource is distributed under the Creative Commons Zero v1.0 Universal Public Domain Dedication (CC0-1.0) license. | | Categories | Inference & Serving, LLM Frameworks, Model Training | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aikit](/tools/kaito-project-aikit.md) | [awesome-free-llm-apis](/tools/mnfst-awesome-free-llm-apis.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 29d | | Open issues (now) | 37 | 17 | | Stars delta | +5 (30d) | +1.3k (30d) | | Open issues delta | -6 (30d) | -8 (30d) | | Full report | [trust report](/tools/kaito-project-aikit/trust.md) | [trust report](/tools/mnfst-awesome-free-llm-apis/trust.md) | ## 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. ## Decision facts: awesome-free-llm-apis - **Pricing:** freemium - The repository lists APIs with permanent free tiers, detailing rate and token limits for those interested in using free Large Language Model services. - **Requirements:** Before integration, ensure that the specific requirements for API access are met to utilize these models without restriction.; Users should review the rate and token limits associated with each API provider to determine suitability based on their project's needs. - **Adopt for:** awesome-free-llm-apis curates permanent free Large Language Model APIs with notable details on their limitations and access methods. - **License detail:** This resource is distributed under the Creative Commons Zero v1.0 Universal Public Domain Dedication (CC0-1.0) license. ## Choose when ### Choose aikit if… - aikit is primarily Go; awesome-free-llm-apis is JavaScript. - License: aikit is MIT, awesome-free-llm-apis is CC0-1.0. - Tags unique to aikit: ai, buildkit, chatgpt, docker. - Also covers LLM Frameworks, Model Training. - aikit ships Docker support for self-hosted deployment. - - You need a flexible solution specifically built using Go and prefer its concurrency model. ### Choose awesome-free-llm-apis if… - awesome-free-llm-apis is primarily JavaScript; aikit is Go. - License: awesome-free-llm-apis is CC0-1.0, aikit is MIT. - Pricing: The repository lists APIs with permanent free tiers, detailing rate and token limits for those interested in using free Large Language Model services.. - Requirements: Before integration, ensure that the specific requirements for API access are met to utilize these models without restriction.; Users should review the rate and token limits associated with each API provider to determine suitability based on their project's needs.. - Tags unique to awesome-free-llm-apis: ai-agents, anthropic, gemini, llm. - When you need text inference with specialized capabilities like roleplay and storytelling, the Aion Labs API offers a dedicated free tier specifically designed for these purposes. ## 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. ## When NOT to use awesome-free-llm-apis - Avoid using Aion Labs if your project involves heavy commercial activity, given its limited rate and token limits. - Do not use Cohere for projects that will scale beyond 1,000 API calls per month or require credit card details for access to higher-tier plans. - Google Gemini's free tier is inaccessible in the EU/UK/Switzerland regions and requires consent to let Google improve their products through your usage. - Mistral AI should be avoided if you need strict confidentiality, as they use prompts from users' interactions to refine and train additional model iterations. ## Common questions ### What is the difference between aikit and awesome-free-llm-apis? aikit: Fine-tune, build, and deploy open-source LLMs easily!. awesome-free-llm-apis: List of Permanent Free LLM API. See the comparison table for live GitHub stats and shared categories. ### When should I choose aikit over awesome-free-llm-apis? Choose aikit over awesome-free-llm-apis when aikit is primarily Go; awesome-free-llm-apis is JavaScript; License: aikit is MIT, awesome-free-llm-apis is CC0-1.0; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers LLM Frameworks, 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 choose awesome-free-llm-apis over aikit? Choose awesome-free-llm-apis over aikit when awesome-free-llm-apis is primarily JavaScript; aikit is Go; License: awesome-free-llm-apis is CC0-1.0, aikit is MIT; Pricing: The repository lists APIs with permanent free tiers, detailing rate and token limits for those interested in using free Large Language Model services.; Requirements: Before integration, ensure that the specific requirements for API access are met to utilize these models without restriction.; Users should review the rate and token limits associated with each API provider to determine suitability based on their project's needs.; Tags unique to awesome-free-llm-apis: ai-agents, anthropic, gemini, llm; When you need text inference with specialized capabilities like roleplay and storytelling, the Aion Labs API offers a dedicated free tier specifically designed for these purposes. ### 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. ### When should I avoid awesome-free-llm-apis? Avoid using Aion Labs if your project involves heavy commercial activity, given its limited rate and token limits. Do not use Cohere for projects that will scale beyond 1,000 API calls per month or require credit card details for access to higher-tier plans. Google Gemini's free tier is inaccessible in the EU/UK/Switzerland regions and requires consent to let Google improve their products through your usage. Mistral AI should be avoided if you need strict confidentiality, as they use prompts from users' interactions to refine and train additional model iterations. ### Is aikit or awesome-free-llm-apis more popular on GitHub? awesome-free-llm-apis has more GitHub stars (7,852 vs 539). Stars measure visibility, not whether either tool fits your constraints. ### Are aikit and awesome-free-llm-apis open source? Yes - both are open-source projects on GitHub (aikit: MIT, awesome-free-llm-apis: CC0-1.0). ### Where can I find alternatives to aikit or awesome-free-llm-apis? GraphCanon lists graph-backed alternatives at [aikit alternatives](/tools/kaito-project-aikit/alternatives) and [awesome-free-llm-apis alternatives](/tools/mnfst-awesome-free-llm-apis/alternatives) ([aikit markdown twin](/tools/kaito-project-aikit/alternatives.md), [awesome-free-llm-apis markdown twin](/tools/mnfst-awesome-free-llm-apis/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/kaito-project-aikit-vs-mnfst-awesome-free-llm-apis.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aikit or awesome-free-llm-apis? aikit: Very active. awesome-free-llm-apis: 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 aikit and awesome-free-llm-apis? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aikit trust report](/tools/kaito-project-aikit/trust); [awesome-free-llm-apis trust report](/tools/mnfst-awesome-free-llm-apis/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kaito-project-aikit`](/api/graphcanon/graph?tool=kaito-project-aikit) - 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/_