Shortly after deploying a new Kubernetes cluster, one of the first things you will likely want to do is collect some metrics and data about how it operates. There are two projects that are typically used for this, and since they are named similarly it can be confusing to know which one you should use and why.

This post hopes to clear up any confusion between the Kubernetes Metrics Server and kube-state-metrics.

Kubernetes Metrics Server

Likely the first project you will encounter when diving into Kubernetes metrics and monitoring is the Kubernetes Metrics Server.

The Metrics API offers a basic set of metrics to support automatic scaling. This API makes information available about CPU and memory usage for each node and pod in the cluster. If you deploy the Metrics API into your cluster, clients of the Kubernetes API can then query for this information.

The Metrics API gets its data from the metrics pipeline. Depicted below, the metrics pipeline consists of (1) the cAdvisor daemon that collects, aggregates, and exposes container metrics. kubelet (2) is a node agent responsible for managing container resources. It gathers data from cAdvisor and from individual resource metrics that it monitors for each pod. kubelet exposes a summary of pod and node statistics through its summary API (3). metrics-server (4) is a cluster component that collects and aggregates resource metrics pulled from each kubelet. Lastly, the API server (5) serves Metrics API for use by horizontal or vertical pod autoscaler, and by the kubectl top command. Metrics Server is a reference implementation of the Metrics API, but that API can be implemented by alternative monitoring solutions.

The Metrics Server pipeline

For example, you can query for the metrics from a single node in your cluster using the nodes endpoint.

❯ kubectl get --raw "/apis/<the-name-of-your-node>" | jq '.'

The returned value includes CPU and memory usage under the usage key.

  "kind": "NodeMetrics",
  "apiVersion": "",
  "metadata": {
    "name": "<your node name>",
    "creationTimestamp": "2023-03-23T13:43:15Z",
  "timestamp": "2023-03-23T13:43:07Z",
  "window": "20.041s",
  "usage": {
    "cpu": "2080431970n",
    "memory": "3378708Ki"

To view individual pod metrics, use the pods endpoint with the correct namespace.

kubectl get --raw "/apis/<your-pod-name>'.'
  "kind": "PodMetrics",
  "apiVersion": "",
  "metadata": {
    "name": "<your-pod-name>",
    "namespace": "default",
    "creationTimestamp": "2023-03-23T13:50:25Z",
  "timestamp": "2023-03-23T13:50:13Z",
  "window": "15.314s",
  "containers": [
      "name": "opentelemetry-collector",
      "usage": {
        "cpu": "7120567n",
        "memory": "571084Ki"

A complete set of metric data is available through the kubectl top command. You can view node metrics with kubectl top node and pod metrics with kubectl top pod:

$ kubectl top node

NAME                 CPU(cores)   CPU%   MEMORY(bytes)   MEMORY%
kind-control-plane   338m         4%     1662Mi          10%

The output from kubectl top node gives you information about CPU (cores), CPU percentage, memory, and memory percentage. Values are given in using standard metric units

Let’s see what these terms mean:

  • CPU(cores)
    • The unit m in this measure means millicpu, or one thousandth of a CPU unit. 1000m is equal to 1 CPU, so in this example 338m means 33.8% of 1 CPU.
  • CPU%
    • CPU percentage stands for the total CPU usage for the entire node. In this case, the kind-control-plane node is using 4% of its CPU.
  • Memory(bytes)
    • Memory displays the total amount of bytes in use. The unit Mi refers to mebibytes. Typically, the unit M is mega which stands for one million. However, since bits and bytes are typically delineated in powers of two, the unit Mi is used to signify the explicit value 1,048,576. Usually we just refer to this as megabytes. In our example, 1662 megabytes are in use.
  • Memory%
    • Memory percentage stands for the total memory usage for the entire node. Here we are using 10% of available memory on the node

The kubectl top pod command provides similar statistics, at the pod level. However, total percentages are dropped.

$ kubectl top pod

NAME                     CPU(Cores)   MEMORY(Bytes)
nginx-653c7b42sd-7c9ae   3m           1Mi

The top pod command uses the currently configured namespace, but you can also filter by namespace with the --namespace option like kubectl top pod --namespace system or list pods from all namespaces with kubectl top pod --all-namespaces.

The Metrics API only offers the minimum CPU and memory metrics to enable automatic scaling using horizontal pod autoscaling and / or vertical pod autoscaling. It is not a complete monitoring solution, and should not be used if you need accurate usage metrics or more complete monitoring.


kube-state-metrics (KSM) is a simple service that listens to the Kubernetes API server and generates metrics about the state of the objects deployed in the cluster. You can use KSM to view metrics on deployments, nodes, pods, and more. KSM holds an entire snapshot of Kubernetes state in memory and continuously generates new metrics based off of it.

The metrics generated by kube-state-metrics are generated from the Kubernetes API objects without any modification. This ensures that any metrics have the same grade of stability as the Kubernetes API objects themselves. This also means that in certain situations, kube-state-metrics may not show the exact same values as kubectl, which uses heuristics to generate displayed values. In these cases, consider kube-state-metrics to be the accurate data source.

kube-state-metrics gathers data using the standard Kubernetes go client and Kubernetes API. This raw data is used to create snapshot of the state of the objects in Kubernetes cluster.

The kube-state-metrics pipeline

When deployed, kube-state-metrics exposes an API at the /metrics endpoint using the 8080 port that can be used to retrieve the state snapshot. If we install kube-state-metrics and expose the port, we are able to poll the metrics endpoint to get a list of metrics that kube-state-metrics tracks.

For example, if you deployed KSM to the kube-system namespace, you can expose it at the port 30135 (as an example).

❯ kubectl port-forward svc/kube-state-metrics 30135:8080 -n kube-system

Navigating to http://localhost:30135/metrics will list all the metrics tracked by the KSM deployment. However, the information displayed is not very consumable — it is exhaustive over all objects in the cluster, and in a raw format. The metrics exposed are in Prometheus’ native format, and Prometheus can be configured to scrape the /metrics endpoint and store it in its internal time-series database for analysis.

Differences between Metrics Server and kube-state-metrics

Although the Metrics Server seems similar to kube-state-metrics, they are meant for different purposes that are summarized in this table:

Metrics Serverkube-state-metrics
Shows resource utilization of objects (CPU/memory)Shows state of objects (up/available/deleted etc.)
Applies heuristics for easier understandingDisplays raw data from Kubernetes API
Serves data in the Metrics API formatServes data in Prometheus format