CPO Approach

CPO uses ML techniques to analyze past behavior and predict future behavior of each active resource (such as a VM or a Pod).  This is used to optimally determine its revised characteristics such as its sizing and parking schedule and verified using Generative AI.  In doing so, it automatically uses the latest cloud provider SKUs and costs that are specific to your account.

For resizing, CPO offers three distinct objectives for which each active resource can be optimized

Economical

Save Costs by Downsizing 

Enhanced

Improves Performance by Up-sizing 

Balanced

Save Costs without Worsening Performance or Improve Performance without Additional Costs

The analysis and the ensuing recommendation are driven by the objective chosen for each active resource.  This allows different active resources to be optimized for different objectives - Enhanced for important active resources, Economical for low-priority active resources, and Balanced for other active resources. 

When resizing recommendations are applied to active resources, the associated benefits are accrued whether it is cost savings or performance improvement, or both. 

In addition to resizing, costs are reduced by determining and applying parking schedules for active resources so that they are only running when they are used.  

Costs are also reduced by cleaning up orphaned passive resources, previously attached to deleted active resources.