Use Grafana and Prometheus metrics to find wasted Kubernetes CPU and memory, then decide what is safe to change.
Download the workbook, cheatsheet, and examples to turn Kubernetes request and usage metrics into a clear review plan.
Most Kubernetes cost reviews start in Grafana: compare requests to usage, find waste, add a buffer, and hope the change is safe. That works for a few workloads. It breaks when you need to repeat it across real teams and real workloads.
Article
Before you lower a request, know what that value controls. CPU and memory requests affect scheduling, bin-packing, autoscaling, QoS, throttling, OOM behaviour, and how much spare capacity your cluster carries.
Read the article →Article
The same recommendation can be safe for one workload and dangerous for another. Java heaps, Node.js old-space, Go GC, Python workers, sidecars, HPAs, batch jobs, and startup spikes all change the decision.
Read the runtime guide →Dashboard
Use the dashboard to spot workloads worth reviewing: high request-to-usage gaps, throttling risk, memory pressure, restarts, QoS signals, and places where missing context makes the decision unsafe.
View the dashboard →Webinar
Watch the full review flow: start from Grafana and workload metrics, identify suspicious requests, check the risks, estimate whether the savings are real, and turn the finding into something an owner can approve.
Register for the webinar →Download
Move from “this workload looks over-requested” to a clear next step: who owns it, what to change, what it saves, and what could go wrong.
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