KorPro vs CAST AI: Resource Cleanup vs Autoscaling — Which Do You Need?

KorPro and CAST AI are both Kubernetes cost optimization tools, but they solve fundamentally different problems. Choosing between them — or deciding to use both — depends on where your waste is hiding.

This comparison breaks down exactly what each tool does, where they overlap, and how to decide.

The Core Difference

KorPro finds and helps you remove unused Kubernetes resources — orphaned ConfigMaps, Secrets, PVCs, Services, Deployments, and more. It answers: "What exists in my cluster that shouldn't?"

CAST AI optimizes infrastructure — node types, spot instances, bin packing, and autoscaling. It answers: "Am I using the cheapest possible compute for my running workloads?"

These are different layers of the cost stack:

┌─────────────────────────────────────┐
│  Layer 3: Infrastructure (CAST AI)  │  Node types, spot instances, scaling
├─────────────────────────────────────┤
│  Layer 2: Pod Right-Sizing          │  CPU/memory requests and limits
├─────────────────────────────────────┤
│  Layer 1: Resource Cleanup (KorPro) │  Remove orphaned/unused objects
└─────────────────────────────────────┘

Feature Comparison

When to Choose KorPro

KorPro is the right choice when your primary concern is resource sprawl and waste accumulation:

• Your clusters have been running for months/years and you suspect there's accumulated waste from old deployments, abandoned feature branches, and incomplete Helm uninstalls
• You need to reduce your attack surface by finding orphaned Secrets and ServiceAccounts with unused credentials
• You manage clusters across multiple clouds and want a single view of orphaned resources across GKE, EKS, and AKS
• Security and compliance matter — KorPro is self-hosted: the Inspector runs inside your own cluster with read-only RBAC, and cluster data plus cloud credentials never leave your environment. Only the findings you choose to report are sent out, which makes it air-gapped and on-prem friendly
• You want an audit-first approach — nothing is deleted without your explicit review and approval

When to Choose CAST AI

CAST AI is the right choice when your primary concern is compute cost and infrastructure efficiency:

• Your nodes are over-provisioned or under-utilized — CAST AI selects optimal instance types and manages spot/on-demand mix
• You have variable workloads that benefit from autoscaling — CAST AI handles node scaling automatically
• You want automated pod right-sizing — CAST AI adjusts CPU/memory requests based on actual usage
• You're comfortable with an agent that has write access — CAST AI needs to create and terminate nodes

When to Use Both

For most production environments, the best results come from using both tools together:

1. Start with KorPro to clean up the obvious waste — orphaned resources that shouldn't exist at all. This gives you immediate savings with zero risk (audit mode, read-only).
2. Then deploy CAST AI to optimize the infrastructure that's running your actual workloads. Once the waste is removed, CAST AI can focus on right-sizing and autoscaling what's left.

This layered approach avoids a common mistake: optimizing the infrastructure that runs your waste. If you have 50 orphaned PVCs and 20 LoadBalancer Services pointing to nothing, right-sizing your nodes won't help. Remove the waste first, then optimize.

Example Savings Stack

Deployment Comparison

KorPro Inspector

bash
# 30-second install per cluster
helm install korpro-inspector korpro/inspector \
  --namespace korpro \
  --create-namespace

• Read-only RBAC, auto-created by Helm
• Runs as a CronJob (every 6 hours by default)
• Minimal footprint: 256Mi RAM, 100m CPU
• Self-hosted: cluster data stays in your cluster — only chosen findings leave, no credentials shared

CAST AI Agent

bash
# Requires API key and write permissions
helm install castai-agent castai/castai-agent \
  --namespace castai-agent \
  --set apiKey=<your-api-key> \
  --create-namespace

• Requires write access to create/delete nodes
• Continuous agent with cluster-admin-level permissions
• Manages node lifecycle directly

Pricing Comparison

KorPro's pricing is flat and predictable. CAST AI's pricing can scale with your infrastructure size, which makes sense for large compute-heavy environments.

The Bottom Line

KorPro and CAST AI are not competitors — they are complementary tools that address different layers of Kubernetes cost optimization.

• KorPro cleans up what shouldn't be there (orphaned resources, unused objects)
• CAST AI optimizes what should be there (node types, scaling, spot instances)

If you're choosing just one to start with, ask yourself: "Is my biggest problem unused resources piling up, or am I paying too much for the compute that runs my active workloads?" The answer will tell you where to begin.

See the full side-by-side breakdown: KorPro vs CAST AI — Feature Comparison

---

Find Out What's Wasting Money in Your Clusters

Before optimizing infrastructure, clean up the waste. Create your free KorPro account to scan your clusters in minutes with read-only access. See exactly which orphaned resources are costing you money — and get a safe-to-prune checklist before touching anything. Want a guided walkthrough? Contact us.