Observed Signal · May 7, 2026 · Technical Guide · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Kubernetes Cost Cut 60% Without Performance Loss
An engineer published a step-by-step how-to describing techniques that reduced a Kubernetes cluster's monthly cloud bill by about 60% while maintaining performance and availability. The author (Pratik Shinde) details practical actions: right-sizing pod CPU/memory requests using kubectl and Prometheus P95 data, adopting Vertical Pod Autoscaler and Goldilocks, moving noncritical workloads to spot/preemptible nodes, configuring Horizontal Pod Autoscaling with custom metrics, using Cluster Autoscaler with specialized node pools, scheduling nonproduction clusters to sleep, optimizing persistent volumes, and monitoring costs with Kubecost/OpenCost. Reported before/after metrics include monthly cost falling from $1,200 to $480, CPU utilization rising from 22% to 65%, and memory utilization from 35% to 70%. The post was published on 2026-05-07.
Practical, actionable guide for reducing cloud/Kubernetes costs that engineering teams can apply; useful but not industry‑shifting or specific to AdTech/MarTech.
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Key Takeaways & Evidence Grounding
- Author reports ~60% total savings: monthly cost reduced from $1,200 to $480
- CPU utilization improved from 22% to 65% and memory utilization from 35% to 70%
- Techniques used include right-sizing requests/limits, VPA/Goldilocks, spot/preemptible nodes, HPA (min 2, max 10), Cluster Autoscaler with node pools, and scheduling non-prod clusters to sleep
- Tools and projects referenced: kubectl, Prometheus, Goldilocks, Vertical Pod Autoscaler (VPA), Kubecost, OpenCost, kube-downscaler
- Author reports dev-cluster sleep scheduling saved ~40% of dev cluster costs; spot instances cited as 70–90% cheaper than on-demand for noncritical workloads
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Run k3s Kubernetes on Hetzner for ~€10
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