Actionable strategies to reduce EKS and GKE cluster spend by 50%+ using intelligent autoscaling, right-sizing, and event-driven KEDA.
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Most development and staging clusters sit at less than 20% average resource utilization while paying for 100% of provisioned EC2 or Compute Engine capacity. This waste typically stems from bloated CPU request reservations, slow node autoscalers, and reluctance to leverage Spot instances.
Karpenter provisions compute directly through AWS APIs without managing EC2 Auto Scaling Groups. When a pod is pending, Karpenter evaluates the pod's exact CPU, memory, and topology spread constraints and launches the most cost-effective instance type in seconds.
By configuring Karpenter NodePools with multi-family diversity (e.g. c5.large, c6i.large, m5.large, m6i.large), the cluster avoids Spot pool exhaustion and tolerates termination notices gracefully.
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How to design, deploy, and monitor fully autonomous agentic workflows on localized hardware without relying on external cloud LLM APIs.
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A deep empirical benchmark comparing throughput, memory footprints, reasoning fidelity, and tool-calling accuracy across open-weights LLMs.
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A complete guide to architecting stateful multi-agent workflows using persistent checkpoints, cross-thread memory, and semantic recall.