It’s easy to manage a Kubernetes cluster by overprovisioning: say, if it hosts only a single application and money is no object. But that’s not the reality, especially when it comes to large Kubernetes deployments shared by dozens of tenants who each have limited budgets. In this more common scenario, the cluster costs must be divided fairly and accurately between the teams, projects, and applications that share the Kubernetes resources.
The cost allocation report for a Kubernetes cluster with a static configuration and two tenants with well-labeled resources is straightforward to calculate. But once you introduce autoscaling into the equation, it becomes impossible for a person to allocate the costs of a Kubernetes cluster to dozens of tenants as autoscaling changes the underlying cluster configuration every minute.
It helps ease the administrative burden if you dedicate a Kubernetes namespace to each tenant, but the challenge of measuring the respective usage of dedicated—and especially shared—resources remains complicated. A fair allocation of costs must consider each tenant’s pro-rated usage of the cluster resources over time, including CPU, GPU, memory, disk, and network.
The Kubecost open source project was conceived to meet this very challenge—providing a simple way to measure cluster resource consumption for applications and teams, and breaking down their respective costs by Kubernetes concepts such as deployment, service, namespace, and label for reporting. Kubecost is always free to use within one cluster of any size and can be downloaded