The Pod Was Cheaper. The Service Wasn’t.

The walkthrough presents an auditable Kubernetes sizing method that combines OpenCost allocation data with proxymock traffic replay to ensure that lower resource allocations reduce cost per successful request without degrading application behavior or throughput. It frames allocation cost divided by successful requests as a FinOps metric useful for showback and chargeback, while noting that production accounting must still account for commitments, discounts, idle capacity, storage, and networking. In a local minikube-based lab, a baseline pod is tested through functional and load replays, with OpenCost measuring CPU and memory costs and proxymock detecting stable response differences, failures, throughput, and latency. An initial reduction in CPU and memory is rejected because CPU throttling sharply lowers throughput and raises unit cost despite acceptable latency, while a final candidate that preserves CPU and reduces oversized memory passes functional checks, maintains nearly equivalent performance, and lowers total cost per successful request by 13.03%. The process emphasizes using identical, recorded traffic conditions and fixed measurement windows so that sizing decisions are supported by reproducible evidence rather than resource-cost estimates alone.

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