Kubernetes Cost Optimization Case Study: How to Prove Savings With Real Data
Blog post from Cast AI
A credible Kubernetes cost optimization case study requires a detailed and documented baseline of at least 30 days, capturing metrics such as monthly compute spend, node count, CPU and memory utilization, Spot instance percentage, OOM kill rate, and engineer hours spent on infrastructure management. Key elements for a convincing case study include presenting multi-metric proof, naming specific mechanisms for cost savings, and providing a clear timeframe for results. Case studies from companies like Akamai, Wio Bank, and NielsenIQ illustrate savings ranging from 40% to 80% depending on workload profiles, emphasizing repeatable and mechanism-backed savings. The use of named spokespeople and honest caveats, such as varying savings by workload type, enhances the credibility of these claims. The guide suggests structuring case studies into six sections: context and scale, baseline metrics, applied mechanisms, results, rollout timeline, and spokesperson quotes to effectively communicate savings and ROI to both engineering and financial audiences.
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