# Workload Drilldown Overview The **Workloads** tab gives you a focused lens into how individual workloads are consuming resources and contributing to your Kubernetes costs. From spotting inefficient deployments to uncovering potential savings, this view helps you take targeted action where it matters most. ## Navigating the View When you land on the Workloads, you're looking at cost and efficiency data with filtering and time-range controls available at the top. You have: * **Group by Namespace**\ To group your workloads by namespace to align insights with how your teams or applications are structured. * **Filters**\ To refine your view using filters like workload type, efficiency levels, or potential savings to zero in on specific problem areas. * **Date Range**\ To set a custom date range to analyze trends over time. This affects all usage metrics and cost graphs in the view. ## Key Metrics At a glance, the top-level metrics give you quick insight into your environment: * **Namespaces**\ Shows how many namespaces are currently running workloads during your selected period. * **Potential Savings**\ Tells you how much cost you could potentially save by right-sizing your workloads based on current usage patterns. * **Workload Efficiency**\ Provides an overall percentage representing how well your workloads are utilizing their allocated resources cost. ## Exploring the Workload List Next up, you'll find a list of all workloads within the scope of your filters. Each row is a workload that you can explore further.
Here’s what each column tells you: | Column | Description | | ---------------------------- | ---------------------------------------------------------------------------------------------------------------------------------- | | **Namespace** |

The Kubernetes namespace where the workload is running.

Click the name to open a namespace-level breakdown.

| | **Workload Name** | The name of the workload | | **Workload Type** | Indicates if it’s a Deployment, StatefulSet, DaemonSet, etc. | | **Efficiency** | Calculated based on CPU and memory utilization vs. requested values. | | **Total Cost** | Total spend associated with the workload during the selected timeframe. | | **Potential Savings** | Estimated savings from optimizing CPU or memory allocations. | | **CPU Utilization (p99)** | Peak CPU usage observed 99% of the time (in millicores). | | **Memory Utilization (p99)** | Peak memory usage observed 99% of the time (in MB). |
Use this table to spot inefficient workloads, high spenders, or those with savings opportunities. When a workload catches your eye, click its row to dive into the details.
## Diving Into a Specific Workload Once you **click into a workload name**, you’ll see everything you need to understand about its resource behavior and cost impact. You’ll start with key identifiers: * **Workload Name** * **Workload Type** * **Namespace** * **Labels** (Automatically pulled from your Kubernetes metadata.)
### Overview Tab This section summarizes how the workload is built and how it's performing. * **Workload Specification**\ See the number of replicas currently deployed for this workload. * **Resource Utilization (Last 30 Days)** CPU and memory usage are visualized over time, compared against the requested and limit values. You have the option to select from the following statistical views. * Based on the choice, the graphs for both **CPU (in millicores)** and **memory (in MB)** will be updated, helping you understand how resource allocation compares to actual usage.
Statistical Views Available | Option | Description | | ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **p99** | 99th percentile – the usage value **below which 99% of data points fall**. Useful for understanding consistent high usage without being affected by rare spikes. | | **p95** | 95th percentile – balances typical usage and peaks, ideal for sizing buffers. | | **Max** | Shows the absolute highest value observed. | | **Min** | Displays the lowest recorded value, highlighting underuse. | | **Average** | Provides the overall mean usage over time. |
* **Daily Cost**\ You can track how a workload’s cost changes over time using adjustable date ranges. Compare recent trends with historical patterns, identify any cost spikes, and evaluate whether optimizations have had an impact. The data is available both as **a visual trend graph** and in a **detailed breakdown table**.
### Container-Level Insights Every workload is made up of containers — and here’s where you get to see their individual impact. For each container tab, you’ll find: * **Resource Utilization (Last 30 Days)** * CPU usage in millicores * Memory usage in MB * **Daily Cost** * Grouped by resource type option * Visualized with total and average cost graphs * Backed by a detailed table for container-by-container breakdown
With this view, you can identify which containers are over-provisioned or which are driving cost spikes — giving you clear direction for optimization. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.onelens.cloud/observe-visibility-and-insights/service-cost-monitoring/kubernetes-visibility/workload-drilldown.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.