Guide 
If you’re running Kubernetes at scale, you’ve likely encountered this paradox: your clusters are drowning in unused capacity while developers insist they need even more resources. CPU and memory sit idle while cloud bills climb. Meanwhile, application owners push back on any suggestion to right-size their workloads, fearing the slightest reduction will crash their services. You’re caught in the middle.
This is the resource overprovisioning problem, and it’s costing organizations millions of dollars annually. Studies consistently show that 40-60% of requested Kubernetes resources go unused. In environments running GPU workloads for AI and machine learning, the waste can be even more staggering, with expensive accelerators sitting idle between jobs.
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