Guide 
If you’ve spent any time managing Kubernetes at enterprise scale, you already know the fundamental tension: your clusters are full of unused capacity, your cloud bills keep climbing, and every attempt to right-size workloads runs headlong into organizational resistance and technical complexity.
You’ve likely tried the manual approach. You’ve pulled metrics from Prometheus, calculated averages and percentiles in spreadsheets, and made recommendations to application teams. It worked for a handful of workloads. Then you looked at the other 500 applications in your environment and realized you’d never catch up.
Maybe you pivoted to rule-based policies. Set quotas. Defined limit ranges. Created standards like “memory requests must be within 20% of limits.” The rules made sense for typical microservices but broke down for batch jobs, caching layers, GPU workloads, and every other edge case. Developers spent their time fighting policies rather than building features. Exception requests piled up faster than you could review them.
The problem isn’t a lack of effort. It’s that Kubernetes resource optimization is a fundamentally different kind of challenge than what manual analysis or static
rules can address. . .continue reading.
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