Workloads

The Workloads report allows you to see your cluster costs broken down by workload. You can use this data to analyze compute expenses per workload or selected group of workloads, identify inefficiencies, and discover potential savings opportunities.

The Workloads report provides four different views:

List of workloads with cost details per selected period,
List of workloads with efficiency details,
Individual workload cost details with daily history,
Individual workload efficiency details with daily history.

List of workloads with cost details per selected period

This is a list of all workloads in your cluster, together with their cost details shown within the period you chose.

Each entry contains the following:

workload name,
workload controller type,
namespace,
average number of pods,
average requested CPU,
average requested RAM,
cost of CPU,
cost of RAM,
the total cost of compute.

You can filter workloads by their labels and namespaces.

To see the total cost of multiple workloads, select the relevant workloads by ticking the box on the left side of the table. You’ll get the total cost data for this group of workloads at the bottom.

List of workloads with efficiency details

This is a list of all cluster workloads with their efficiency details per selected period.
Each entry contains the following:

workload name,
workload controller type,
namespace,
CPU hours wasted,
memory hours wasted,
$ wasted (the amount of money wasted, in US dollars).

Each entry also contains the requested and used resource hours.

The metric we use here is resource hours, which corresponds to resources multiplied by hours of usage.

For example, if a workload with requests set to 2 CPUs runs for 48 hours during the selected period, its total requested CPU hours would be 96 CPU hours. If a workload's average CPU usage is 0.5 CPU during those 48 hours, its total usage is 24 CPU hours.

CAST AI managed mode customers can also use a quick recommendations patch that they can apply to their workload and change workload resource requests in line with our recommendations.

How do we calculate the wasted CPUs, RAM, and money?

To calculate the number of wasted CPU and RAM resources, we subtract the workload’s used resources from the total number of requested resources.

To follow the example above:

96 CPU hours - 24 CPU hours = 72 CPU hours

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Note
For better recommendations and information on efficiency, set the requests based on the workloads within your cluster.

Individual workload cost details with daily history

This report provides cost details and the history of a single workload within the selected period.

You get the following data:

total spend,
current month forecast,
average daily cost,
average daily cost per resource (CPU and RAM).

Daily compute spend

You can also check the daily chart of compute spend per resource and per lifecycle.

Daily cost history table

Daily cost history table with average pod count, cost per pod, amount of requested resources, cost per resource, and total cost.

This report provides efficiency details for a single workload: both current and within the selected period.

Individual workload efficiency details with daily history

This report provides efficiency details for a single workload: both current and within the selected period.

Current workload efficiency

The current efficiency part provides current efficiency details per container:

resource requests,
resource usage,
rightsizing recommendation,
computed overall efficiency.

Computed efficiency is calculated by comparing resource requests against the recommended rightsized resource values. CPU is more expensive and has a larger impact on efficiency ratings. Learn more about this here.

For rightsizing recommendations, the Cost monitoring module analyzes the resource use of a container during the last 5 days and calculates the percentile value (95th percentile for CPU and 99th percentile for RAM).