# kaito-project/kaito

Kubernetes AI Toolchain Operator

Repository: https://github.com/kaito-project/kaito
Canonical: https://ross.abutalabs.com/products/kaito
Homepage: https://kaito-project.github.io/kaito/docs/
Language: Go
License: NOASSERTION
License Family: other
Topics: ai, gpu, kubernetes, operator
Last push: 2026-09-02T21:04:13+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 99, longevity 77
- inputs: {"age_days": 1090, "days_push": 0, "days_rel": 6, "gap_med": 21.0, "n_releases_24m": 27}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1009, forks 183 (observed 2026-09-03T02:15:13.946290+00:00)

## What it is
KAITO is a Kubernetes operator suite that automates LLM inference, fine-tuning, and RAG engine deployment using simplified CRD APIs. It auto-provisions GPU nodes with memory-based estimation and supports any vLLM-compatible HuggingFace model.

## Use cases
- deploy llm inference on kubernetes
- fine-tune llm models in a cluster
- run vllm models on gpu nodes
- auto-provision gpu nodes for model serving
- deploy rag engine in kubernetes
- distributed multi-node llm inference
- simplify llm deployment with crd

## When to choose
- you run Kubernetes and want declarative LLM inference or fine-tuning workloads
- you want automatic GPU node provisioning with memory estimation
- you need preset-optimized parallelism settings instead of hand-tuning vLLM parameters
- you want to serve any vLLM-supported HuggingFace model

## When to avoid
- you don't use Kubernetes
- you need a lightweight single-machine inference server
- you need fine-grained control over every inference engine parameter
- your cluster cannot provision GPU nodes

## Facets
- artifact type: service
- maturity: active
- function: llm-inference, llm-training, rag, container-orchestration, deployment, gpu-computing
- domain: large-language-models, machine-learning, cloud-computing, infrastructure-as-code
- platform: go, cloud, self-hosted
- tags: kubernetes-operator, vllm, fine-tuning, gpu-provisioning, crd, distributed-inference, karpenter, kubernetes, docker

## Member repositories
- kaito-project/kaito (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:13.946290+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:13:53.554537+00:00, confidence not recorded.
  - readme: https://github.com/kaito-project/kaito (fetched 2026-09-03T02:15:13.946290+00:00, sha 00a30b6d78f0)
  - homepage: https://kaito-project.github.io/kaito/docs/ (fetched 2026-08-29T13:13:39.133472+00:00, sha de893d1e5402)
- Data as of 2026-08-30T08:39:29.467469+00:00.