Version: 1.0.1 (Latest)

Overview

Kamiwaza is a Kubernetes-based AI platform that combines model serving, governed data access, application deployment, and security controls behind a single customer-facing domain. This page provides a high-level view of the current architecture used in supported deployments.

System Architecture Diagram

Compute and Orchestration

• Kubernetes
• Ray and Serving Runtimes
• Extension Operator
• CPU and GPU Nodes
• Shared Platform Layer
• Traefik or Istio Ingress
• Identity Provider
• Postgres or SQLite in lite mode
• etcd
• DataHub and Catalog Services
• Object Storage
• Platform Services
• API and Auth Gateway
• Model and Serving Services
• DDE, Retrieval, and Catalog
• Workrooms and Collaboration
• Logger and Audit Services

Architecture Layers

Experience layer

This is how users and client applications interact with Kamiwaza.

• Kamiwaza Web UI for administration, models, workrooms, apps, tools, and logs
• SDK and API clients for programmatic access
• App Garden and Tool Shed for launching applications and tool servers behind managed routes

Services layer

This layer contains the platform APIs and business logic.

• API and Auth Gateway for authenticated access, routing, and policy enforcement
• Model and Serving Services for model lifecycle and inference
• DDE, Retrieval, and Catalog for ingestion, discovery, secret references, and retrieval flows
• Workrooms and Collaboration for shared workspace functionality
• Logger and Audit Services for runtime troubleshooting and security evidence

Shared platform layer

This layer provides the shared services the platform depends on.

• Traefik or Istio for ingress and routing, depending on environment
• Identity provider integration for authenticated deployments
• Postgres as the standard persistent database in auth-enabled deployments
• SQLite as a reduced-scope database option in lite mode
• etcd for cluster coordination and runtime configuration
• DataHub and related catalog services for metadata-backed catalog workflows
• Object storage for uploaded files, workroom context, and related assets

Compute and orchestration layer

This layer runs the workloads that power the platform.

• Kubernetes as the primary deployment target
• Ray and serving runtimes for model execution and distributed work
• Extension Operator for managed application and tool deployment workflows
• CPU and GPU nodes that host inference and platform workloads

Technology Stack

Design Principles

• Single customer-facing domain with path-based runtime routing as the standard deployment model
• Security by configuration through identity integration, access controls, consent, and auditability
• Composable services so model serving, retrieval, catalog, apps, and tools can evolve independently
• Operational visibility through deployment logs, cluster logs, and OTEL-compatible export paths