How to find your allocated vCPU?

Learn how to retrieve the number of CPU Units per Kubernetes node to find your perfect license

To provide a more accurate cost metric and improve pricing transparency, the PerfectScale solution is licensed by the monthly vCPU consumption of your clusters.

If your usage remains under 300 vCPU per month during your trial, you will be automatically converted to the free community package. Learn more about the Pricing model here.

Determine vCPU consumption

There are a few ways to determine your vCPU consumption:

In the PerfectScale platform

In your observability tool

In-app

PerfectScale by DoiT has built a valuable feature that provides both the team and customers seamless access to subscription details, including visibility into normalized monthly vCPU consumption right within the platform.

To access your license details, go to Subscription Details, located under the account button at the bottom left of the screen.

Subscription details

To view more details and track monthly vCPU consumption over time, simply click View over-time vCPU consumption report to access granular data in a trend report.

Monthly vCPU trend

Observability tools

With your observability tool, like Grafana and DataDog you can either use some existing dashboard or create a new graph summarizing cluster capacity. For example, Prometheus can be used to create a visualization of cluster capacity, as shown below:

    SUM(kube_node_status_capacity{resource="cpu", unit="core"})

vCPU/hours in the observability tool

In the above example, the allocated vCPU is around 90 cores. We can estimate monthly core hours as 90*24*30 = 64800

Use the terminal following the guide below:

• Open a new terminal on the machine that has kubectl been installed. Make sure you are running in the correct context (=cluster)

• Type the following command on the prompt:

kubectl get nodes -o custom-columns=NAME:'{.metadata.name}',CORES:'{.status.capacity.cpu}'

This will show you the currently running instances and their core capacity at a single point in time:

NAME                                             CORES
ip-10-107-0-11.eu-central-1.compute.internal     2
ip-10-107-12-213.eu-central-1.compute.internal   2
ip-10-107-24-124.eu-central-1.compute.internal   8
ip-10-107-24-142.eu-central-1.compute.internal   2
ip-10-107-26-193.eu-central-1.compute.internal   2
ip-10-107-31-30.eu-central-1.compute.internal    8
ip-10-107-32-253.eu-central-1.compute.internal   4
ip-10-107-32-47.eu-central-1.compute.internal    4
ip-10-107-37-121.eu-central-1.compute.internal   2
ip-10-107-4-114.eu-central-1.compute.internal    2
ip-10-107-40-28.eu-central-1.compute.internal    4
ip-10-107-45-210.eu-central-1.compute.internal   2
ip-10-107-47-136.eu-central-1.compute.internal   2
ip-10-107-47-184.eu-central-1.compute.internal   4
ip-10-107-7-175.eu-central-1.compute.internal    4
ip-10-107-7-204.eu-central-1.compute.internal    2

• Now you can summarize the cores to have cluster cores right now. You can multiply this number by 24*30 to have a monthly estimate.

If your cluster has a dynamic nature, this approach may be less accurate than Observability tools or PerfectScale.