| name | castai-core-workflow-b |
| description | Configure CAST AI Workload Autoscaler for pod-level right-sizing and VPA.
Use when enabling workload autoscaling, configuring resource recommendations,
or tuning pod CPU and memory requests with CAST AI.
Trigger with phrases like "cast ai workload autoscaler", "cast ai pod sizing",
"cast ai resource recommendations", "cast ai VPA".
|
| allowed-tools | Read, Write, Edit, Bash(curl:*), Bash(kubectl:*), Grep |
| version | 1.4.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| tags | ["saas","kubernetes","cost-optimization","castai"] |
| compatibility | Designed for Claude Code |
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
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
apiVersion: apps/v1
kind: Deployment
metadata:
name: my-api
annotations:
autoscaling.cast.ai/enabled: "true"