Abhishek Veeramalla

Top 5 Ways to Reduce Kubeneres Cluster Cost.

The video walks viewers through five proven tactics for slashing Kubernetes cluster expenses, whether the workloads run on public clouds or on‑premise hardware. Abhishek emphasizes that unchecked compute—especially GPU nodes running at single‑digit utilization—can bleed money, making cost discipline essential for every DevOps team. First, he urges granular cost visibility at the pod, deployment, service, namespace, node, and cluster levels, recommending tools like Kubecost, OpenCost, or even simple Excel tracking. Second, he stresses right‑sizing pods with explicit resource requests and limits to avoid runaway consumption from memory leaks or misbehaving services. Third, he highlights autoscaling—not just scaling up but also scaling down—using native cloud features (EKS Auto‑mode, AKS Auto, GKE Autopilot) or third‑party solutions such as Karpenter or Cast AI to match capacity to demand. He then recommends moving non‑critical, stateless workloads to spot instances, which can shave 60‑70% off compute bills, and illustrates this with an e‑commerce email microservice that tolerates occasional delays. Finally, he warns about hidden cross‑zone networking fees, using a Kafka multi‑AZ deployment as an example where inter‑AZ replication incurs extra charges, and suggests consolidating storage (e.g., S3) or redesigning architectures to avoid them. Taken together, these practices enable organizations to transform Kubernetes from a cost‑center into a cost‑optimized platform, driving measurable savings while maintaining performance and reliability. Implementing visibility, right‑sizing, autoscaling, spot usage, and network‑cost awareness can reduce cloud spend dramatically and free budget for innovation.