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How It Works
When engineers size cloud resources, they often add buffer to avoid performance risk. A virtual machine is set to 16 vCPUs when the application rarely exceeds 4. A database instance is configured for peak traffic that arrives a few days per year. Across dozens or hundreds of services, these individual decisions compound into significant wasted spend. The pattern is common because cloud platforms make it easy to scale up and difficult to justify scaling down. Teams prioritize availability and performance, and right-sizing resources to actual usage requires ongoing measurement and deliberate action that many organizations deprioritize.
Why It Matters for Cloud Cost
Over-provisioned resources incur full on-demand charges regardless of actual utilization. A server running at 10% CPU still costs the same as one running at 90%. Across a mid-sized cloud environment, this waste frequently represents a material share of the total bill. The problem accelerates as teams grow: new services are provisioned conservatively, old ones are rarely reviewed, and development and test environments are cloned from production specs and then forgotten. Without active monitoring and rightsizing, over-provisioning compounds month over month. Identifying and correcting it is one of the highest-leverage cost reduction actions available before layering on commitment-based discounts.
Usage AI: ClearCost gives teams the visibility and showback reporting needed to identify where over-provisioned resources are driving unnecessary spend across the organization.