Kubernetes Cluster Cost Estimator
Estimate your monthly K8s costs on AWS EKS, Google GKE, and Azure AKS.
Kubernetes is powerful, but its costs can be complex and opaque. Use our calculator to estimate your monthly bill by configuring your worker nodes and cloud provider. Make informed infrastructure decisions and avoid budget overruns for your containerized applications.
Kubernetes Cluster Cost Estimator
Estimate your monthly Kubernetes costs on AWS (EKS), GCP (GKE), and Azure (AKS).
About This Tool
The Kubernetes Cost Estimator is a vital tool for DevOps engineers, platform teams, and financial planners managing cloud-native infrastructure. Kubernetes has become the de facto standard for container orchestration, but calculating its true cost is notoriously difficult. The total bill is a combination of a fixed cluster management fee (which varies by provider) and the variable cost of the underlying compute instances (worker nodes) that run your applications. This calculator demystifies the pricing by breaking it down into these core components. By allowing you to select your preferred cloud provider (AWS EKS, Google GKE, or Azure AKS), choose your worker node instance types, and specify the number of nodes, it provides a clear and actionable monthly cost estimate. This empowers teams to budget accurately, compare the TCO of different providers, right-size their clusters, and make data-driven decisions to optimize their cloud spend without sacrificing performance or availability.
How to Use This Tool
- First, select your cloud provider: AWS, GCP, or Azure.
- Next, choose the instance type for your worker nodes from the dropdown. This includes options for general purpose, compute-optimized, and GPU instances.
- Use the slider to set the number of worker nodes you plan to have in your cluster.
- Click the "Calculate Kubernetes Costs" button.
- The tool will display a breakdown of your estimated monthly bill, separating the cluster management fee from the worker node compute costs.
In-Depth Guide
The Two Main Costs of Managed Kubernetes
Your monthly bill for a managed Kubernetes service like EKS, GKE, or AKS is primarily composed of two parts. First is the control plane fee: a fixed hourly rate for the management infrastructure that the cloud provider runs for you. This is what makes it a 'managed' service. As you can see in the calculator, Azure makes this free, while AWS and GCP charge for it (though GKE has a generous free tier). The second, and usually much larger, cost is for the worker nodes. These are simply standard virtual machine instances that you pay for at their normal on-demand rates.
Right-Sizing Your Nodes and Pods
A common mistake is to request far more CPU and memory in your pod specifications than your application actually needs. This leads to 'stranded' resources—CPU and memory on a node that is paid for but cannot be allocated because no single pod can fit into the remaining space. Setting accurate resource requests and limits for your pods is crucial. This allows the Kubernetes scheduler to pack pods onto nodes efficiently, letting you run more applications on fewer nodes, which directly saves money.
The Hidden Costs: Storage and Networking
This calculator focuses on the primary compute costs, but a complete TCO analysis must also include networking and storage. Every time your service needs a public IP address, you use a Load Balancer, which has an hourly cost. When your pods need to persist data, they use Persistent Volumes, which are backed by cloud storage disks (like EBS or a managed file store) and have their own monthly fee. Finally, any data that leaves the cluster for the public internet will incur egress fees. These 'hidden' costs can add up quickly and must be factored into your budget.
Cost Optimization Strategies: FinOps for Kubernetes
Beyond autoscaling and right-sizing, advanced teams use several other strategies. 'Node bin packing' is the art of scheduling pods as densely as possible to minimize the number of running nodes. Using tools like Karpenter (for AWS) or GKE Autopilot can automate much of this. Additionally, analyzing your usage patterns and committing to 'Reserved Instances' or 'Savings Plans' for your baseline compute needs can provide significant discounts over on-demand pricing.