kaito
Kubernetes AI Toolchain Operator for managing and scaling inference workloads
GraphCanon updated 3w · GitHub synced 3w
Decision brief
Kaito is a Kubernetes AI Toolchain Operator that facilitates the deployment and scaling of AI models in production environments using Helm or Terraform.
Good fit when
- When you need to integrate HuggingFace runtime for BYO models within your Kubernetes environment, as KAITO specifically supports models hosted there.
- If auto-scaling is required for inference workloads, leveraging KEDA integration provides optimized performance and resource management.
Avoid when
- Avoid if your organization prefers open-source model hosting that does not include HuggingFace; KAITO mandates use of the HuggingFace ecosystem.
- Do not use when a custom autoscaling solution outside of KEDA is needed, as KAITO integrates tightly with KEDA for its scaling capabilities.
- Requirements:
- Requires Docker
Observed Jul 16, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (1d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- 2 low (2 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
go get github.com/kaito-project/kaitoSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A Kubernetes operator that enables the deployment, scaling, and management of AI models in a production environment using Helm or Terraform.
Capability facts
- Languages
- go, python
Source: github.language+pyproject.toml · Aug 2, 2026
Categories
Tags
README
Getting Started
- Installation: Please check the guidance here for installing core components (Workspace, InferenceSet) using helm and here for installation using Terraform.
- Quick Start: Please check the quick start guidance here for running your first model using KAITO!
- AutoScaling: Please check this doc for configuring KAITO and KEDA to enable autoscaling inference workload.
- BYO models using HuggingFace runtime: If you plan to run any BYO models using the HuggingFace runtime, check this doc. Note: KAITO only supports BYO models hosted in HuggingFace.
- CPU models: Please check this doc for running CPU models using aikit.
- RAGEngine: Please check the installation guidance and usage documents here.
- RAGEngine Output Guardrails: Please check the current behavior, configuration, and limitations here.
- Prefill/Decode Disaggregation: Please check this doc for deploying models with P/D disaggregation using MultiRoleInference and Gateway API Inference Extension.
License
See Apache License 2.0.
For agents
This page has a .md twin and JSON over the API.