Optimize Kubernetes Costs with Node Pool Recommendations

Updated 1 month ago by Archana Singh

One of the most impactful ways to reduce spend on Kubernetes infrastructure is to make sure your clusters are optimally sized for the workloads and node pools they run. Harness CCM recommends optimal compute resources for your workloads and node pools. It can help you reduce costs and improve performance, by analyzing your historical utilization metrics. CCM recommends you choose the optimal resource configuration based on your utilization data and requests metrics of Pods.

This topic describes how CCM computes node pool recommendations and how you can use them to potentially reduce monthly costs.

Before using recommendations in your cluster environment, ensure that you evaluate their impact thoroughly. The person reviewing the recommendations should be able to understand the impacts identified in the recommendations, as well as the impact on the infrastructure and business.


Using recommendations without proper assessment could result in unexpected changes, such as issues with system performance or poor reliability.

In this topic:

Before You Begin

How are Node Pool Recommendations Computed?

The node pool recommendations are computed by analyzing historical utilization data and requests metrics of Pods. CCM recommends the optimal resource configurations for the Spot and On-demand instances. It uses the following parameters to determine the maximum node counts:

  • Total CPUs
  • Total memory
  • Max CPUs
  • Max Memory

CCM also offers the flexibility of modifying the resource configurations. You can modify the value of CPU, memory, and node counts. In such a scenario, CCM generates context-aware cluster sizing recommendations based on the resource configurations that you provide.

View Recommendations

Once you enable CCM, it may take up to 48 hours for the recommendations to appear in Cloud Costs. It depends on the time at which CCM receives the utilization data for the workload.
  1. In Cloud Costs, click Recommendations.

    The recommendations page displays the following information:
    • A breakdown of all the available recommendations.
    • Potential Monthly Savings across your Kubernetes clusters if you apply the recommendations.
    • Forecasted Monthly Spend across your Kubernetes clusters if you do not apply the recommendations.
  2. The Recommendation Breakdown displays the following information:

    Monthly Savings

    Potential monthly savings for your resource, if you apply the recommendations.

    Resource Name

    Name of the resource for which CCM displays the recommendation.

    Resource Type

    Type of the resource for which the CCM has provided the recommendations. For example, node pool or workload.

    Monthly Cost

    Potential Monthly Savings for the resource, if you apply the recommendations.

    Recommendation Type

    Type of the recommendation for your resource. For example, rightsizing or resizing. Based on your resource type, CCM recommends to rightsize or resize your CPU, memory, or node counts.

  3. Click the recommendation for which you want to view the details. You can use filter to select the resource or recommendation for which you want to view the details.

    You can filter by:
    • Name: Each Kubernetes namespace in the cluster.
    • Resource Type: The type of resources for which the recommendation is displayed. Currently, CCM supports node pool and workload.
    • Namespace: Each Kubernetes namespace in the cluster.
    • Cluster Name: Each Kubernetes cluster in your infrastructure.
    • Monthly savings greater than: Filter by potential monthly savings greater than the specified amount. For example, all the recommendations that provide potential monthly savings more than $1000.
    • Monthly cost greater than: Filiter by forecasted monthly spend greater than the specified amount. For example, all the recommendations that provide forecasted monthly spend more than $1000.
  4. Recommendation for the selected resource is displayed.
  5. You can use this information to optimize your resources to potentially reduce your monthly cloud costs.

Next Steps


Please Provide Feedback