Kubernetes Cost Optimization RFP Template: Requirements, Questions, and Scoring
Blog post from Cast AI
Kubernetes cost optimization involves addressing multiple layers of waste, including CPU and memory overprovisioning, node fragmentation, and idle GPU hours, with potential savings ranging from $46K to $64K for a $100K/month compute budget. To effectively evaluate vendors, it's crucial to distinguish between visibility tools like Kubecost and OpenCost, and autonomous optimization platforms such as Cast AI and Spot.io, with a focus on automation depth. A comprehensive RFP should include eight requirement categories, emphasizing automation and production safety, and employ a weighted scoring matrix for vendor assessment. Cost visibility, rightsizing, and node autoscaling are key areas, with rightsizing identified as carrying the highest weight due to prevalent overprovisioning issues. The RFP process should start with written questionnaires before demos to ensure accountability and should run parallel evaluations across multiple vendors, including a mandatory 2-week proof of concept in actual production clusters to avoid surprises related to vendor claims and ensure realistic savings projections.
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