--- title: "aikit vs fastDeploy" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kaito-project-aikit-vs-notai-tech-fastdeploy" tools: ["kaito-project-aikit", "notai-tech-fastdeploy"] --- # aikit vs fastDeploy *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 fastDeploy if fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines. [aikit](https://kaito-project.github.io/aikit/) reports 539 GitHub stars, 57 forks, and 37 open issues, last pushed Sep 18, 2026. [fastDeploy](https://github.com/notAI-tech/fastDeploy) has 105 stars, 17 forks, and 0 open issues, last pushed Feb 10, 2026. Figures are from public GitHub metadata via [aikit's repository](https://github.com/kaito-project/aikit) and [fastDeploy's repository](https://github.com/notAI-tech/fastDeploy). | | [aikit](/tools/kaito-project-aikit.md) | [fastDeploy](/tools/notai-tech-fastdeploy.md) | | --- | --- | --- | | Tagline | Fine-tune, build, and deploy open-source LLMs easily! | Deploy DL/ML inference pipelines with minimal extra code. | | Stars | 539 | 105 | | Forks | 57 | 17 | | Open issues | 37 | 0 | | Language | Go | Python | | Adopt for | Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. | fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | 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) | [fastDeploy](/tools/notai-tech-fastdeploy.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 221d | | Open issues (now) | 37 | 0 | | Stars delta | +5 (30d) | 0 (30d) | | Open issues delta | -6 (30d) | 0 (30d) | | Full report | [trust report](/tools/kaito-project-aikit/trust.md) | [trust report](/tools/notai-tech-fastdeploy/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: fastDeploy - **Pricing:** freemium - - - **Requirements:** - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory. - **Adopt for:** fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines. ## Choose when ### Choose aikit if… - aikit is primarily Go; fastDeploy is Python. - Tags unique to aikit: ai, buildkit, chatgpt, fine-tuning. - 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 fastDeploy if… - fastDeploy is primarily Python; aikit is Go. - Pricing: -. - Requirements: - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory.. - Tags unique to fastDeploy: deep-learning, falcon, gevent, gunicorn. - When you aim to streamline the deployment of TensorFlow Serving, TorchServe, and Triton Inference Server models without extensive coding. ## 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 fastDeploy - Avoid if you are looking for a solution that supports real-time interactive deployments requiring advanced websocket handling beyond fastDeploy's basic capability. - Not recommended when the project requires heavy customization of deployment scripts, as it emphasizes minimal coding and may restrict flexibility in pipeline configurations. ## Common questions ### What is the difference between aikit and fastDeploy? aikit: Fine-tune, build, and deploy open-source LLMs easily!. fastDeploy: Deploy DL/ML inference pipelines with minimal extra code.. See the comparison table for live GitHub stats and shared categories. ### When should I choose aikit over fastDeploy? Choose aikit over fastDeploy when aikit is primarily Go; fastDeploy is Python; Tags unique to aikit: ai, buildkit, chatgpt, fine-tuning; 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 fastDeploy over aikit? Choose fastDeploy over aikit when fastDeploy is primarily Python; aikit is Go; Pricing: -; Requirements: - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory.; Tags unique to fastDeploy: deep-learning, falcon, gevent, gunicorn; When you aim to streamline the deployment of TensorFlow Serving, TorchServe, and Triton Inference Server models without extensive coding. ### 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 fastDeploy? Avoid if you are looking for a solution that supports real-time interactive deployments requiring advanced websocket handling beyond fastDeploy's basic capability. Not recommended when the project requires heavy customization of deployment scripts, as it emphasizes minimal coding and may restrict flexibility in pipeline configurations. ### Is aikit or fastDeploy more popular on GitHub? aikit has more GitHub stars (539 vs 105). Stars measure visibility, not whether either tool fits your constraints. ### Are aikit and fastDeploy open source? Yes - both are open-source projects on GitHub (aikit: MIT, fastDeploy: MIT). ### Where can I find alternatives to aikit or fastDeploy? GraphCanon lists graph-backed alternatives at [aikit alternatives](/tools/kaito-project-aikit/alternatives) and [fastDeploy alternatives](/tools/notai-tech-fastdeploy/alternatives) ([aikit markdown twin](/tools/kaito-project-aikit/alternatives.md), [fastDeploy markdown twin](/tools/notai-tech-fastdeploy/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-notai-tech-fastdeploy.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aikit or fastDeploy? aikit: Very active. fastDeploy: Slowing. 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 fastDeploy? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aikit trust report](/tools/kaito-project-aikit/trust); [fastDeploy trust report](/tools/notai-tech-fastdeploy/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/_