k8s-resource-analyzer

To optimize Kubernetes pod resources, you analyze current CPU and memory usage against configured requests and limits to reveal inefficiencies and guide safe, cost-aware adjustments. This process computes P95 usage over configurable time windows to generate concrete recommendations.

Determining HPA adoption safety requires analyzing Kubernetes pod resource usage against allocations to ensure sufficient headroom. By computing P95 CPU and memory metrics, you can identify HPA candidates and perform safety checks before scaling.

You need Prometheus and Grafana metrics to analyze Kubernetes resource usage effectively. Querying Prometheus metrics via Grafana MCP computes P95 CPU and memory usage over configurable windows, which is required to generate accurate optimization recommendations.

You can analyze Kubernetes pod resource usage across multiple clusters, specifically supporting multi-cluster workflows for prod-green and prod-white environments. It orchestrates pod discovery and metrics collection across these configured clusters to produce a comprehensive report.

The best way to analyze Kubernetes CPU and memory limits for cost optimization is to fetch pod definitions via kubectl access and query Prometheus metrics to compute P95 usage. This reveals allocation inefficiencies and produces a structured recommendations report.

To access Kubernetes pod definitions for resource analysis, you need Friday MCP for kubectl access. This allows the analysis workflow to fetch resource requests and limits per deployment across your configured clusters.