AI Kubernetes Recommendations

The Savings Opportunities page provides an AI based list of cost optimization recommendations for Kubernetes workloads. Recommendations are generated automatically based on resource utilization and potential cost savings.

Each row in the table represents an individual opportunity, with details such as:

Opportunity Name and Type: Describes the recommended action (e.g., rightsizing a node, deployment, or pod).
Description: Summarizes the current situation that triggered the recommendation, such as underutilized resources or over-provisioned workloads.
Resource Type: Identifies the Kubernetes resource (Node, Pod, Deployment, DaemonSet, StatefulSet).
Namespace: Indicates the Kubernetes namespace where the resource resides.
Implementation Effort: Provides a relative measure of the complexity involved in applying the recommendation.
Cluster: Shows the associated Kubernetes cluster.
Monthly Savings: Displays the estimated cost savings if the recommendation is implemented.

At the top of the AI Kubernetes Savings page, several key metrics summarize the potential impact of applying recommendations:

**Potential Monthly Savings **– shows the estimated amount you can save if you implement the listed recommendations.
Recommendation Value vs. Effort – groups recommendations by the level of effort required (Low, Medium, High) and shows the total savings potential for each category.

Drawers

By clicking on the Opportunity it will open the Drawer where you can see the important data. Here you have abilities like:

Select Grouping
- Resource
- Product Family
- Usage Type
- Item Description
Change the Data range
Set a Granularity
- Daily
- Weekly
- Monthly
- Quarterly
- Yearly
Change the Chart view
Set a Cost Type
See the Important Details about the particular instance

AI Description

When you hover over the description under the Description column it tells you the reason why you should take the recommendation, what impact you will get after it and the alternative way.