diff --git a/content/en/docs/tasks/debug/debug-cluster/resource-usage-monitoring.md b/content/en/docs/tasks/debug/debug-cluster/resource-usage-monitoring.md
index adc63ece40..90054fafa4 100644
--- a/content/en/docs/tasks/debug/debug-cluster/resource-usage-monitoring.md
+++ b/content/en/docs/tasks/debug/debug-cluster/resource-usage-monitoring.md
@@ -16,7 +16,7 @@ application performance in a Kubernetes cluster by examining the containers,
 the characteristics of the overall cluster. Kubernetes provides detailed
 information about an application's resource usage at each of these levels.
 This information allows you to evaluate your application's performance and
-where bottlenecks can be removed to improve overall performance.
+where bottlenecks can be removed to improve overall performance. 
 
 <!-- body -->
 
@@ -59,6 +59,24 @@ Autoscaler. The monitoring pipeline fetches metrics from the kubelet and
 then exposes them to Kubernetes via an adapter by implementing either the
 `custom.metrics.k8s.io` or `external.metrics.k8s.io` API.
 
+
+Kubernetes is designed to work with [OpenMetrics](https://openmetrics.io/), 
+which is one of the
+[CNCF Observability and Analysis - Monitoring Projects](https://landscape.cncf.io/card-mode?category=monitoring&project=graduated,incubating,member,no&grouping=category&sort=stars),
+built upon and carefully extending [Prometheus exposition format](https://prometheus.io/docs/instrumenting/exposition_formats/)
+in almost 100% backwards-compatible ways.
+
+If you glance over at the
+[CNCF Landscape](https://landscape.cncf.io/card-mode?category=monitoring&project=graduated,incubating,member,no&grouping=category&sort=stars), 
+you can see a number of monitoring projects that can work with Kubernetes by _scraping_
+metric data and using that to help you observe your cluster. It is up to you to select the tool
+or tools that suit your needs. The CNCF landscape for observability and analytics includes a
+mix of open-source software, paid-for software-as-a-service, and other commercial products.
+
+When you design and implement a full metrics pipeline you can make that monitoring data
+available back to Kubernetes. For example, a HorizontalPodAutoscaler can use the processed
+metrics to work out how many Pods to run for a component of your workload.
+
 Integration of a full metrics pipeline into your Kubernetes implementation is outside
 the scope of Kubernetes documentation because of the very wide scope of possible
 solutions.
@@ -69,6 +87,7 @@ Your monitoring system should be capable of handling the [OpenMetrics](https://o
 transmission standard, and needs to chosen to best fit in to your overall design and deployment of
 your infrastructure platform.
 
+
 ## {{% heading "whatsnext" %}}