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.
Prometheus OpenCost Exporter Helm Chart
The Prometheus Opencost Exporter is available within the Prometheus Community Kubernetes Helm Charts repository. This provides the Prometheus metric exporter capabilities without the OpenCost UI or any other additional capabilities. It is maintained as part of the regular OpenCost release process and regularly updated.
Installing Manually
Note: all deployments of OpenCost function as a Prometheus metric exporter. View recommended install.
Follow these steps to set up OpenCost as exporter-only:
-
Apply the combined YAML:
kubectl apply --namespace opencost-exporter \
-f https://raw.githubusercontent.com/opencost/opencost/develop/kubernetes/exporter/opencost-exporter.yaml -
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
static_configs:
- 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
| Metric | Description |
|---|---|
| container_cpu_allocation | Percent of a single CPU used in a minute |
| container_gpu_allocation | GPU used |
| container_memory_allocation_bytes | Bytes of RAM used |
| kubecost_cluster_info | ClusterInfo |
| kubecost_cluster_management_cost | Hourly cost paid as a cluster management fee. |
| kubecost_load_balancer_cost | Hourly cost of load balancer |
| kubecost_network_internet_egress_cost | Total cost per GB of internet egress. |
| kubecost_network_region_egress_cost | Total cost per GB egress across regions |
| kubecost_network_zone_egress_cost | Total cost per GB egress across zones |
| kubecost_node_is_spot | Cloud provider info about node preemptibility |
| node_cpu_hourly_cost | hourly cost for each cpu on this node |
| node_gpu_count | count of gpu on this node |
| node_gpu_hourly_cost | hourly cost for each gpu on this node |
| node_ram_hourly_cost | hourly cost for each gb of ram on this node |
| node_total_hourly_cost | Total node cost per hour |
| pod_pvc_allocation | Bytes used by a PVC attached to a pod |
| pv_hourly_cost | Cost 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:
You can find other example dashboards at https://grafana.com/orgs/kubecost
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
avg_over_time(container_memory_allocation_bytes{namespace="kube-system",pod=~"fluentd.*"}[1d])
* 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
Limitations
- For large clusters, these Prometheus queries might not scale well over large time windows.
- Allocation metrics, like
container_cpu_allocationonly contain requests and do not take usage into account. - Related to the previous point, efficiency metrics are not available.
