Architecture Overview

KubeOpera is composed of two codebases that work together: a Next.js frontend and a set of Go microservices. Communication is via REST, RabbitMQ topic exchanges, and Kubernetes CRDs.

System Diagram

High-Level System Architecture

Select any component to see what it does

User InterfaceWho works in KubeOpera

API GatewayNext.js server-side proxy

Central Management PlaneGo microservices and AI analytics

Application Services

Analytics Engine

Kubernetes-as-a-ServiceManaged clusters and tenant spaces

Control Plane

etcdcontroller-managercloud-controller-managerschedulerkube-apiservercrm

Work Plane

KubeSpace 1

BillCtlrResourceCtlr

AI Agents

vCluster

apikubeletetcd

NodePool 1 · 4 nodes

KubeSpace 2

BillCtlrResourceCtlr

AI Agents

vCluster

apikubeletetcd

NodePool 2 · 4 nodes

KubeSpace 3

BillCtlrResourceCtlr

AI Agents

vCluster

apikubeletetcd

NodePool 3 · 4 nodes

KubeSpace 4

BillCtlrResourceCtlr

AI Agents

vCluster

apikubeletetcd

NodePool 4 · 4 nodes

Platform ToolsDelivery, observability and identity

KubeOpera by Ochestra · ochestra.io

Select any component above to learn what it does.

Event Flow

Data moves through the platform via RabbitMQ topic exchanges. The reactive AI pipeline detects problems, takes action, and measures results autonomously, without human intervention — detection, analysis, and action form a real, working chain. The final piece, closing the loop so the system tunes its own detection thresholds based on whether its actions actually helped, has the logic built on both ends but isn't wired together yet — see the diagram below for exactly where.

Reactive AI Pipeline — Event Flow

Autonomous detection → action → feedback. The loop back to detection isn't wired up yet.

MetricPoint

observability.telemetry

Decisions

analysis.decisions

action.outcomes

feedback.signals · no consumer

Insights

analysis.insights

Select any service to see its role in the event pipeline.