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Kubernetes Monitoring (with Prometheus) Dashboard

The Kubernetes monitoring dashboard is a great example of what is possible with SquaredUp beyond SCOM and Azure! This dashboard combines live metrics from Prometheus to give a performance overview of a Kubernetes cluster using SquaredUp’s PowerShell and WebAPI tiles.

Team, SquaredUp


More organizations than ever are taking advantage of the efficiency benefits that container-based architectures provide in the cloud, and Kubernetes is by far the most popular orchestration tool used in production environments today.

However, with Kubernetes there is a steep learning curve, a constantly evolving ecosystem, and multiple layers of abstraction that generate a vast number of metrics; monitoring Kubernetes can be challenging.


This Kubernetes monitoring dashboard pulls live metrics from Prometheus using a range of SquaredUp’s PowerShell and WebAPI visualizations to provide a performance overview of a Kubernetes cluster. It can be easily modified to suit the needs of any monitoring team.

Surfacing meaningful metrics in a visually beautiful way, this working example dashboard enables quick identification of problems that are brewing in the Kubernetes cluster. The dashboard can be used as is, or adapted to support any metrics being scraped into Prometheus by simply changing the underlying queries (PromQL) used in the dashboard tiles.

Dashboard walk-through

At a glance, you can see how the cluster is performing as a whole in this Kubernetes monitoring dashboard. Scroll down to see the performance of individual nodes as necessary.

At the very top we are displaying information that identifies the name and type of cluster that is being monitored, as well as the network traffic over time.

The next section focuses on the memory, CPU and disk utilization of the cluster. We can see immediately when the cluster is nearing maximum capacity with the Status and Donut visualizations, as well as how those metrics are trending over time with the Line Graph.

The final section breaks out those critical metrics by individual nodes, helping to identify problematic nodes which maybe adversely impacting the performance of the overall cluster.


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