Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/castai-deploy-integration/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill castai-deploy-integration. Copyright stays with the author (MIT).

CAST AI Deploy Integration

Overview

Deploy CAST AI to EKS, GKE, and AKS clusters using official Terraform modules. Each cloud provider has a dedicated CAST AI module that handles IAM roles, node configuration, and autoscaler setup.

Prerequisites

• Terraform 1.0+
• CAST AI Full Access API key
• Cloud provider credentials configured
• Existing Kubernetes cluster

Instructions

EKS Deployment

# main.tf -- EKS cluster onboarding
module "castai_eks" {
  source  = "castai/eks-cluster/castai"
  version = "~> 3.0"

  api_token           = var.castai_api_token
  aws_account_id      = data.aws_caller_identity.current.account_id
  aws_cluster_region  = var.region
  aws_cluster_name    = var.cluster_name

  # IAM role for CAST AI to manage nodes
  aws_instance_profile_arn = aws_iam_instance_profile.castai.arn

  # Autoscaler configuration
  autoscaler_policies_json = jsonencode({
    enabled = true
    unschedulablePods = { enabled = true }
    nodeDownscaler = {
      enabled = true
      emptyNodes = { enabled = true, delaySeconds = 300 }
    }
    spotInstances = {
      enabled = true
      spotDiversityEnabled = true
    }
    clusterLimits = {
      enabled = true
      cpu = { minCores = 4, maxCores = 200 }
    }
  })

  # Node templates
  default_node_configuration = module.castai_eks.castai_node_configurations["default"]
}

GKE Deployment

module "castai_gke" {
  source  = "castai/gke-cluster/castai"
  version = "~> 2.0"

  api_token            = var.castai_api_token
  project_id           = var.gcp_project_id
  gke_cluster_name     = var.cluster_name
  gke_cluster_location = var.region

  gke_credentials = base64decode(
    google_container_cluster.this.master_auth[0].cluster_ca_certificate
  )

  autoscaler_policies_json = jsonencode({
    enabled = true
    unschedulablePods = { enabled = true }
    nodeDownscaler = {
      enabled = true
      emptyNodes = { enabled = true, delaySeconds = 300 }
    }
  })
}

AKS Deployment

module "castai_aks" {
  source  = "castai/aks/castai"
  version = "~> 1.0"

  api_token              = var.castai_api_token
  aks_cluster_name       = var.cluster_name
  aks_cluster_region     = var.region
  node_resource_group    = azurerm_kubernetes_cluster.this.node_resource_group
  azure_subscription_id  = data.azurerm_subscription.current.subscription_id
  azure_tenant_id        = data.azurerm_client_config.current.tenant_id

  autoscaler_policies_json = jsonencode({
    enabled = true
    unschedulablePods = { enabled = true }
    spotInstances = { enabled = true }
  })
}

Multi-Cluster Deployment Pattern

# Deploy CAST AI across all clusters with a for_each
variable "clusters" {
  type = map(object({
    name     = string
    provider = string  # eks, gke, aks
    region   = string
    max_cpu  = number
  }))
}

# Then reference the appropriate module per provider

Error Handling

Output

Produce a reviewed infrastructure plan identifying cloud, cluster, module and provider versions, policy limits, secret references, and the per-cluster rollout decision. Deployment evidence must show the intended cluster identity and health after apply; never treat a successful Terraform exit code as proof that the autoscaler is safe to enable.

Examples

Deploy one staging EKS cluster using a pinned module version and conservative policy limits, then verify agent health and policy state through the provider API. Promote separate GKE or AKS clusters only after their own plans and approvals pass; on a bad IAM or policy result, revert the changed state through the reviewed Terraform workflow rather than applying ad-hoc console changes.

Resources

• EKS Module
• GKE Module
• AKS Module
• CAST AI Terraform Provider

Next Steps

For webhook-based automation, see castai-webhooks-events.