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OpenCost as a Prometheus metric exporter

Running OpenCost as a Prometheus metric exporter allows you to export various cost metrics to Prometheus without setting up any other OpenCost dependencies. Doing so lets you write PromQL queries to calculate the cost and efficiency of any Kubernetes concept, e.g. namespace, service, label, deployment, etc. You can also calculate the cost of different Kubernetes resources, e.g. nodes, PVs, LoadBalancers, and more. Finally, you can do other interesting things like create custom alerts via AlertManager and custom dashboards via Grafana.


Note: all deployments of OpenCost function as a Prometheus metric exporter. View recommended install.

Follow these steps to set up OpenCost as exporter-only:

  1. Apply the combined YAML:

    kubectl apply --namespace opencost-exporter \
  2. To verify that metrics are available:

    kubectl port-forward --namespace opencost-exporter service/opencost 9003

    curl http://localhost:9003/metrics to see exported metrics

Add OpenCost scrape config to your Prometheus (more info)

- job_name: opencost
scrape_interval: 1m
scrape_timeout: 10s
metrics_path: /metrics
scheme: http
- targets: ['opencost.opencost-exporter:9003']

OpenCost is now exporting cost metrics. See the following sections for different metrics available and query examples.

Available Prometheus Metrics

container_cpu_allocationPercent of a single CPU used in a minute
container_gpu_allocationGPU used
container_memory_allocation_bytesBytes of RAM used
kubecost_cluster_management_costHourly cost paid as a cluster management fee.
kubecost_load_balancer_costHourly cost of load balancer
kubecost_network_internet_egress_costTotal cost per GB of internet egress.
kubecost_network_region_egress_costTotal cost per GB egress across regions
kubecost_network_zone_egress_costTotal cost per GB egress across zones
kubecost_node_is_spotCloud provider info about node preemptibility
node_cpu_hourly_costhourly cost for each cpu on this node
node_gpu_countcount of gpu on this node
node_gpu_hourly_costhourly cost for each gpu on this node
node_ram_hourly_costhourly cost for each gb of ram on this node
node_total_hourly_costTotal node cost per hour
pod_pvc_allocationBytes used by a PVC attached to a pod
pv_hourly_costCost per GB per hour on a persistent disk

By default, all cost metrics are based on public billing APIs. See the Limitations section below about reflecting your precise billing information. Supported platforms are AWS, Azure, and GCP. For on-prem clusters, prices are based on configurable defaults.

Dashboard examples

Here’s an example dashboard using OpenCost Prometheus metrics:

sample dashboard

You can find other example dashboards at

Example Queries

Once OpenCost’s cost model is running in your cluster and you have added it in your Prometheus scrape configuration, you can hit Prometheus with useful queries like these:

Monthly cost of all nodes

sum(node_total_hourly_cost) * 730

Hourly cost of all load balancers broken down by namespace

sum(kubecost_load_balancer_cost) by (namespace)

Monthly rate of each namespace’s CPU request

sum(container_cpu_allocation * on (node) group_left node_cpu_hourly_cost) by (namespace) * 730

Historical memory request spend for all fluentd pods in the kube-system namespace

* on (pod,node) group_left
avg(count_over_time(container_memory_allocation_bytes{namespace="kube-system"}[1d:1m])/60) by (pod,node)
* on (node) group_left
avg(avg_over_time(node_ram_hourly_cost[1d] )) by (node)

Setting Cost Alerts

Custom cost alerts can be implemented with a set of Prometheus queries and can be used for alerting with AlertManager or Grafana alerts. Below are example alerting rules.

Determine in real-time if the monthly cost of all nodes is > $1000

sum(node_total_hourly_cost) * 730 > 1000


  • For large clusters, these Prometheus queries might not scale well over large time windows.
  • Allocation metrics, like container_cpu_allocation only contain requests and do not take usage into account.
  • Related to the previous point, efficiency metrics are not available.
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