CAST AI Core Workflow: Workload Autoscaler

Overview

CAST AI Workload Autoscaler right-sizes pod resource requests based on actual usage, reducing over-provisioning without manual VPA tuning. This skill covers enabling the workload autoscaler, configuring scaling policies per workload, and using annotations for fine-grained control.

Prerequisites

• Completed castai-core-workflow-a (cluster-level policies)
• CAST AI agent v1.60+ installed
• Workload Autoscaler enabled in CAST AI console

Instructions

Step 1: Install Workload Autoscaler Components

helm upgrade --install castai-workload-autoscaler \
  castai-helm/castai-workload-autoscaler \
  -n castai-agent \
  --set castai.apiKey="${CASTAI_API_KEY}" \
  --set castai.clusterID="${CASTAI_CLUSTER_ID}"

Step 2: Query Workload Recommendations

# Get resource recommendations for a specific workload
curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/workload-autoscaling/clusters/${CASTAI_CLUSTER_ID}/workloads" \
  | jq '.items[] | {
    name: .workloadName,
    namespace: .namespace,
    currentCpu: .currentCpuRequest,
    recommendedCpu: .recommendedCpuRequest,
    currentMemory: .currentMemoryRequest,
    recommendedMemory: .recommendedMemoryRequest,
    savingsPercent: .estimatedSavingsPercent
  }'

Step 3: Configure Per-Workload Policies via Annotations

# Add annotations to deployments for CAST AI workload autoscaler
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-api
  annotations:
    # Enable workload autoscaling
    autoscaling.cast.ai/enabled: "true"
    # CPU configuration
    autoscaling.cast.ai/cpu-min: "100m"
    autoscaling.cast.ai/cpu-max: "4000m"
    autoscaling.cast.ai/cpu-headroom: "15"
    # Memory configuration
    autoscaling.cast.ai/memory-min: "128Mi"
    autoscaling.cast.ai/memory-max: "8Gi"
    autoscaling.cast.ai/memory-headroom: "20"
    # Apply changes automatically vs recommendation-only
    autoscaling.cast.ai/apply-type: "immediate"
spec:
  template:
    spec:
      containers:
        - name: api
          resources:
            requests:
              cpu: "500m"      # Will be auto-adjusted by CAST AI
              memory: "512Mi"  # Will be auto-adjusted by CAST AI