castai-reference-architecture

name	castai-reference-architecture
description	CAST AI reference architecture for multi-cluster Kubernetes cost optimization. Use when designing CAST AI deployment across environments, planning Terraform module structure, or establishing team standards. Trigger with phrases like "cast ai architecture", "cast ai best practices", "cast ai multi-cluster", "cast ai terraform structure".
allowed-tools	Read, Write, Edit, Grep
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","kubernetes","cost-optimization","castai"]
compatible-with	claude-code

CAST AI Reference Architecture

Overview

Production-grade architecture for managing CAST AI across multiple Kubernetes clusters. Covers Terraform module layout, per-environment policies, API key management, and observability integration.

Prerequisites

• Multiple Kubernetes clusters (dev, staging, production)
• Terraform for infrastructure management
• Centralized secrets management
• Monitoring stack (Prometheus, Grafana, or Datadog)

Terraform Module Structure

infrastructure/
├── modules/
│   └── castai-cluster/
│       ├── main.tf              # CAST AI provider resources
│       ├── variables.tf         # Cluster-specific inputs
│       ├── outputs.tf           # Cluster ID, savings metrics
│       ├── policies.tf          # Autoscaler policy configuration
│       ├── node-templates.tf    # Node template definitions
│       └── security.tf          # Kvisor, RBAC
├── environments/
│   ├── dev/
│   │   ├── main.tf              # Dev cluster onboarding
│   │   ├── terraform.tfvars     # Dev-specific values
│   │   └── backend.tf           # State storage
│   ├── staging/
│   │   ├── main.tf
│   │   ├── terraform.tfvars
│   │   └── backend.tf
│   └── prod/
│       ├── main.tf
│       ├── terraform.tfvars
│       └── backend.tf
└── shared/
    ├── api-keys.tf              # Key management
    └── monitoring.tf            # Alerting rules

Reusable Module

# modules/castai-cluster/main.tf
variable "cluster_name" { type = string }
variable "cluster_id" { type = string }
variable "environment" { type = string }
variable "api_token" { type = string; sensitive = true }
variable "provider_type" { type = string }  # eks, gke, aks
variable "max_cpu_cores" { type = number; default = 100 }
variable "spot_enabled" { type = bool; default = true }
variable "hibernation_enabled" { type = bool; default = false }
variable "evictor_aggressive" { type = bool; default = false }

resource "castai_autoscaler" "this" {
  cluster_id = var.cluster_id

  autoscaler_policies_json = jsonencode({
    enabled = true
    unschedulablePods = {
      enabled = true
      headroom = {
        enabled          = true
        cpuPercentage    = var.environment == "prod" ? 15 : 5
        memoryPercentage = var.environment == "prod" ? 15 : 5
      }
    }
    nodeDownscaler = {
      enabled = true
      emptyNodes = {
        enabled      = true
        delaySeconds = var.environment == "prod" ? 300 : 60
      }
    }
    spotInstances = {
      enabled              = var.spot_enabled
      spotDiversityEnabled = true
    }
    clusterLimits = {
      enabled = true
      cpu     = { minCores = 2, maxCores = var.max_cpu_cores }
    }
  })
}

resource "castai_node_template" "default_spot" {
  cluster_id = var.cluster_id
  name       = "${var.environment}-spot-workers"
  is_enabled = var.spot_enabled

  constraints {
    spot               = true
    use_spot_fallbacks = true
    architectures      = ["amd64"]
  }
}