Why Your Cloud Cost Optimization Tool Is Losing Money

We investigate the paradox of cloud cost optimization platforms, where automated tools designed to save money often increase it through hidden fees and inefficient right-sizing. Using a specific case study of a mid-sized fintech firm that spent more on optimization software than they saved in compute credits, we break down why passive automation fails in dynamic workloads. We look at the real mechanics of how these tools misinterpret spot instance availability and ignore data gravity costs, leading to a net negative return on investment for many engineering teams.

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