Platform Architecture Overview | Kamiwaza Docs

This is documentation for Kamiwaza 0.5.0, which is no longer actively maintained. For the current GA release, see 1.0.1.

Version: 0.5.0

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Kamiwaza is a modular, multi-layered platform designed for scalability, flexibility, and enterprise-grade performance. This document provides a high-level overview of its key architectural components and the technologies that power them.

System Architecture Diagram

The diagram below illustrates the layered architecture of the Kamiwaza platform, from the underlying infrastructure to the user-facing applications. Each layer provides a distinct set of capabilities, creating a robust and maintainable system.

Architecture Layers

🌐 Application Layer

This is where users and developers interact with Kamiwaza.

• Kamiwaza Frontend: The primary web-based user interface for managing the platform.
• Client SDK: A Python SDK for programmatically interacting with Kamiwaza's APIs.
• App Garden: A platform for discovering and deploying pre-packaged AI applications and services.

🎯 Kamiwaza Services Layer

The core business logic of the platform, exposed via a central API gateway.

• FastAPI Gateway: A high-performance API gateway that coordinates all requests.
• Microservices: A suite of specialized services handling concerns like Authentication, Model Management, Vector DB abstraction (Milvus, Qdrant), and more.

🛠️ Core Infrastructure Services

The essential backend services that support the entire platform.

• Traefik Router: A powerful reverse proxy and load balancer that manages all inbound network traffic and provides SSL termination.
• CockroachDB: The primary database—a distributed, resilient SQL database for all application data.
• etcd: A distributed key-value store used for service discovery and critical configuration management across the cluster.

🔧 Orchestration & Compute Foundation

The base layer that provides the compute resources and orchestration to run everything else.

• Ray Serve: A scalable model-serving framework used for deploying and managing AI models.
• Docker Swarm: The container orchestration engine that manages the lifecycle of all containerized services.
• Hardware / Cloud: The physical servers or cloud instances (e.g., AWS, GCP, Azure) that provide the underlying CPU and GPU resources.

Technology Stack