castai-core-workflow-b

# 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 ```bash 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 ```bash # 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 ```yaml # Add annotations to deployments for CAST AI workload autoscaler apiVersion: apps/v1 kind: Deplo...