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Developer Tests CAST AI on GKE to Detect and Fix Kubernetes Over-Provisioning

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A developer built a hands-on lab using Google Kubernetes Engine and CAST AI to explore how Kubernetes cost optimization tools identify over-provisioned workloads. The experiment involved deploying a lightweight FastAPI application that was deliberately assigned far more CPU and memory than it actually consumed — 1000m CPU and 1Gi memory versus actual usage of just a few millicores and megabytes. The setup used Docker, Google Artifact Registry, and a two-replica GKE deployment to simulate a realistic but wasteful production scenario. CAST AI was then connected to the cluster to monitor real resource consumption, compare it against declared requests, and generate rightsizing recommendations. The lab aimed to document the full optimization cycle, from deployment and observation to recommendation and verification, rather than simply installing the tool.

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Developer Tests CAST AI on GKE to Detect and Fix Kubernetes Over-Provisioning · ShortSingh