Kubernetes pricing: Cost factors and 5 ways to reduce expenses

What Is Kubernetes Pricing or Kubernetes Cost? 

Kubernetes pricing is a problematic term, because Kubernetes itself is open source and available for free. However, running Kubernetes incurs costs for organizations—whether they run it on-premises, which incurs hardware and operational costs, or in the cloud, which results in the cost of managed services.

Cloud providers such as AWS, Azure, and Google Cloud offer managed Kubernetes services (e.g., EKS, AKS, GKE), which simplify deployment and maintenance but add service charges. On-premises Kubernetes deployments may save on service costs but involve significant labor, hardware, and maintenance expenses.

Understanding Kubernetes costs and service pricing is crucial for cost management and avoiding unexpected bills, especially in dynamic and scaling environments.

This is part of a series of articles about Kubernetes architecture

In this article:

Factors Influencing Kubernetes Costs

Infrastructure Components

The infrastructure needed for Kubernetes includes compute, memory, and storage resources, typically provisioned through virtual machines (VMs) or bare-metal servers. Costs vary depending on the number and size of nodes in the cluster, the geographical region of the infrastructure, and whether you’re using reserved or on-demand capacity in the cloud.

Managed Kubernetes services also introduce fees for control plane management, which can range from a fixed monthly charge to per-cluster costs. For example, Google Kubernetes Engine charges a flat fee for each cluster, in addition to the compute and storage costs. High availability configurations and features like autoscaling may further increase infrastructure costs.

Resource Usage and Scaling

Kubernetes dynamically allocates resources to workloads based on their requests and limits, which directly impacts costs. Over-provisioning resources for pods leads to underutilization, driving up unnecessary expenses. Under-provisioning can lead to performance issues.

Autoscaling features, such as the horizontal pod autoscaler and cluster autoscaler, help optimize resource usage. These tools adjust the number of pods and nodes based on demand, reducing costs during low-traffic periods. However, they can also cause cost spikes during high-demand periods if not monitored closely.

Storage and Networking Costs

Storage in Kubernetes can be provisioned through persistent volumes (PVs), which are backed by block storage, file storage, or cloud-native storage services. Costs depend on the type, size, and performance characteristics of the storage used. High-performance SSDs or managed cloud storage like AWS EBS or Azure Disk are typically more expensive than lower-tier options.

Networking costs arise from internal and external traffic. For example, data egress charges in cloud environments can quickly escalate when transferring data between regions or across the internet. Load balancers and ingress controllers, often required for exposing applications, add additional costs. These costs are highly variable and need careful analysis based on workload requirements.

Complexity of Shared Resources

In multi-tenant environments, sharing resources between teams or projects can complicate cost tracking and allocation. Kubernetes namespaces and resource quotas help manage this complexity by isolating workloads and capping resource usage per team or application.

However, shared cluster components like monitoring tools, logging services, and ingress controllers may incur shared costs that are hard to attribute. Transparent cost reporting and allocation mechanisms, such as those provided by Kubernetes cost-management tools, are crucial for handling shared resource complexities effectively.

Multiple Environments and Clusters

Running multiple Kubernetes environments—such as development, staging, and production—or maintaining multiple clusters for different regions or workloads increases costs. Each environment or cluster requires its own resources, which might include separate control planes, networking, and storage.

Consolidating workloads into fewer clusters can reduce costs but may introduce operational challenges, such as ensuring workload isolation. Alternatively, managed Kubernetes platforms with multi-cluster management features can simplify operations but often add management fees.

Hidden Costs in Kubernetes Deployments 

In addition to the factors directly shaping Kubernetes costs, there are some hidden costs associated with deploying in Kubernetes.

Operational Overheads

Operational overheads in Kubernetes deployments refer to the labor and resources needed to manage and maintain clusters effectively. Even with managed Kubernetes services, tasks like configuring security policies, setting up CI/CD pipelines, and handling upgrades require significant expertise.

On-premises deployments demand even more effort, including provisioning hardware, managing networking, and ensuring high availability. These operations require a skilled DevOps or SRE team, which adds to personnel costs.

The constant evolution of Kubernetes, with frequent updates and feature changes, necessitates continuous learning and adaptation, increasing training costs. These factors collectively contribute to hidden operational overheads that organizations must account for when planning Kubernetes deployments.

Monitoring and Logging Expenses

Monitoring and logging are critical for managing Kubernetes workloads, but they come with their own costs. Tools like Prometheus, Grafana, and ELK Stack can be deployed in-cluster, but they consume significant compute and storage resources, increasing infrastructure costs.

Alternatively, using managed solutions such as Datadog, New Relic, or AWS CloudWatch introduces subscription fees that scale with usage. The granularity of metrics and the retention period for logs also influence costs, with high-resolution metrics and long retention leading to higher expenses. These expenses can grow rapidly in large or high-traffic clusters.

Learn more in our detailed guide to Kubernetes monitoring

Difficulty of Implementing Chargebacks or Showbacks

In multi-tenant Kubernetes environments, assigning costs to individual teams or applications is challenging due to the shared nature of cluster resources. Chargeback (billing teams for their actual usage) or showback (reporting usage costs without billing) mechanisms require precise tracking of resource utilization, often involving complex integrations with monitoring and cost management tools.

Tools like Kubecost and CloudHealth can provide insights, but they require additional configuration and may charge their own fees. Accurately tracking shared resources such as ingress controllers or monitoring systems adds to the complexity. Without effective cost allocation, organizations risk inefficiencies and disputes over resource usage.

Examples of Managed Kubernetes Pricing 

When evaluating managed Kubernetes services, it’s crucial to understand their pricing structures to estimate costs accurately. Here’s a breakdown of three main services and their pricing components:

Amazon Elastic Kubernetes Service (EKS):

  • Cluster management fee: EKS charges $0.10 per hour for each Kubernetes cluster for standard support or $0.60 per hour for extended support.
  • Worker nodes: Customers pay for the AWS resources used by the worker nodes, such as EC2 instances and EBS volumes. For example, a t3.medium EC2 instance in the N. Virginia region costs approximately $0.00499 per hour, translating to about $3.59 per month if run continuously.
  • Fargate integration: If users opt for AWS Fargate to run the pods, pricing is based on the vCPU and memory resources utilized from the time users start downloading the container image until the pod terminates. A minimum charge of one minute applies.

Azure Kubernetes Service (AKS):

  • Cluster management fee: AKS offers a free tier with basic services, including a $200 credit to try any other paid service.
  • Node pricing: Customers pay for the virtual machines (VMs) that serve as the worker nodes. For example, a Standard_D2_v2 VM in the East US region costs approximately $0.146 per hour. Running a single such VM continuously for a month would cost around $106.58.
  • Additional services: Charges may apply for other Azure services integrated with the AKS cluster, such as load balancers, storage, and networking resources.

Google Kubernetes Engine (GKE):

  • Cluster management fee: GKE charges $0.10 per cluster per hour for the Standard mode. This fee covers the management of the Kubernetes control plane. For Enterprise mode, clusters are billed at $0.00822 per vCPU per hour.
  • Autopilot mode: In Autopilot mode, GKE manages the underlying infrastructure, and customers are billed based on the CPU, memory, and storage resources requested by the pods. There is a flat fee of $0.10 per cluster per hour.
  • Node pricing: In Standard mode, customers pay for the Compute Engine instances (VMs) that function as the worker nodes. The cost varies based on the machine type and region. For example, an n1-standard-4 VM in the us-central1 region costs approximately $0.1899 per hour.
  • Additional costs: Other potential expenses include persistent storage, network egress, and load balancing, depending on the cluster’s configuration and usage.

5 Best Practices for Optimizing Kubernetes Costs [QG1]

By implementing these best practices, organizations can ensure that their Kubernetes spending matches their needs.

1. Rightsizing Resources

To reduce waste and avoid unnecessary expenses, ensure that resource requests and limits are appropriately set for each workload. Analyze historical usage data to determine the actual CPU and memory requirements, and adjust pod specifications accordingly. Tools like Kubernetes Metrics Server or third-party solutions can provide insights into real-time and historical resource utilization.

Avoid over-provisioning resources, as unused capacity incurs costs without adding value. Conversely, under-provisioning can lead to performance issues and service interruptions. Periodic reviews and adjustments of resource settings help maintain an optimal balance between cost and performance.

2. Utilizing Spot Instances

Spot instances (or preemptible VMs) offered by cloud providers are significantly cheaper than on-demand instances, making them an excellent choice for non-critical or batch workloads. These instances can be terminated by the provider with short notice, so they are unsuitable for workloads requiring high availability.

To safely use spot instances, configure Kubernetes node pools to include both spot and on-demand instances. Assign low-priority workloads to spot instances using taints and tolerations or node selectors. Managed services like AWS EKS, Google GKE, and Azure AKS offer integrations that simplify the use of spot instances in Kubernetes clusters.

3. Implementing Auto Scaling

Kubernetes’ horizontal pod autoscaler (HPA) and cluster autoscaler (CA) allow workloads and infrastructure to scale dynamically based on demand, reducing costs during low-traffic periods. HPA adjusts the number of pod replicas based on metrics like CPU or memory usage, while CA adds or removes nodes depending on cluster capacity requirements.

Proper configuration is crucial to avoid cost spikes or under-provisioning during peak traffic. Define sensible thresholds for scaling and monitor their impact on costs and performance. Using predictive scaling based on historical trends can further optimize resource utilization.

4. Optimizing Storage Usage

Efficient storage usage can significantly lower costs. Use appropriate storage classes for different workloads to ensure the company is not overpaying for performance levels it doesn’t need. For example, high-performance SSDs should be reserved for latency-sensitive applications, while lower-cost options like HDDs can suffice for archival or backup storage.

Periodically clean up unused persistent volumes (PVs) and reclaim storage associated with terminated workloads. Employ retention policies to manage log and backup storage, and use compression or deduplication to minimize storage consumption.

5. Implementing Cost Monitoring and Reporting

Cost monitoring tools provide visibility into resource consumption and associated costs, enabling informed decision-making. Tools like Kubecost, CloudHealth, and native solutions from cloud providers integrate with Kubernetes to provide granular insights into cluster usage and expenses.

Set up cost reports to track trends and identify inefficiencies, such as underutilized nodes or expensive storage configurations. Use tagging and labeling to associate costs with teams, projects, or applications, enabling better cost allocation and accountability. Regularly review these reports to identify optimization opportunities and ensure alignment with budgetary goals.

Automating Kubernetes Infrastructure with Spot by NetApp

Spot Ocean from Spot by NetApp frees DevOps teams from the tedious management of their cluster’s worker nodes while helping reduce cost by up to 90%. Spot Ocean’s automated optimization delivers the following benefits:

  • Container-driven autoscaling for the fastest matching of pods with appropriate nodes
  • Easy management of workloads with different resource requirements in a single cluster
  • Intelligent bin-packing for highly utilized nodes and greater cost-efficiency
  • Cost allocation by namespaces, resources, annotation and labels
  • Reliable usage of the optimal blend of spot, reserved and on-demand compute pricing models
  • Automated infrastructure headroom ensuring high availability
  • Right-sizing based on actual pod resource consumption  

Learn more about Spot Ocean today!