GKE Pricing Models Explained and 4 Ways to Optimize Your Costs

What Is Google Kubernetes Engine (GKE)?

Google Kubernetes Engine (GKE) is a managed platform that allows developers to deploy, manage, and scale containerized applications using Kubernetes. It is a part of Google Cloud Platform (GCP) and provides a fully managed, highly available, and secure environment for running containerized workloads.

With GKE, developers can easily create and manage Kubernetes clusters, scale up or down resources based on demand, and deploy containerized applications with ease using the gcloud CLI. GKE also integrates with other GCP services, such as Google Cloud Storage and Cloud SQL, to provide a complete solution for building and deploying cloud-native applications.

GKE is built on top of Google Compute Engine (GCE) instances, which are used to run the worker nodes in a Kubernetes cluster. It removes the complexity of managing infrastructure and allows developers to focus on writing code, making it easier to build, deploy, and manage containerized applications at scale.

In this article:

Google Kubernetes Engine Pricing Explained

Here is an overview of the different pricing models for GKE.

Free Tier

Google Kubernetes Engine offers a free tier that allows users to get started with the platform at no cost. The free tier includes a 744-hour allocation of Autopilot cluster usage per month, which allows users to run a managed Kubernetes cluster without worrying about the underlying infrastructure. Users also receive a $74 credit to use towards any GCP service, including GKE.

In addition to the free Autopilot cluster hours and credit, GKE also has a cluster management fee that varies based on the type of GCP account. For account_1, which is a free trial account, the cluster management fee is $0. For account_2, which is a paid GCP account, the fee is $100 per cluster per month. For account_3, which is a committed use account, the fee is $225 per cluster per month.

It’s important to note that the free tier is limited and may not be sufficient for production-level workloads. The 744-hour allocation of Autopilot cluster usage per month equates to approximately 31 days of continuous usage of a single node. Additionally, users should be aware of any costs associated with running workloads on GKE, such as the cost of running worker nodes, storage, and other GCP services that may be required.

Autopilot Mode

The Autopilot mode provides a more streamlined and automated experience for running Kubernetes clusters. With Autopilot, Google manages the underlying infrastructure and handles tasks such as scaling, node upgrades, and security patching, allowing users to focus on deploying and managing their containerized applications.

Autopilot pricing is a flat $0.10/hour fee per cluster and covers the cost of cluster management and infrastructure, including memory, CPU, and ephemeral storage. There is no minimum duration for using Autopilot, and users can start and stop clusters as needed.

Here is an example for the US-West1 region:

Standard Mode

Standard mode provides more control and flexibility over the configuration and management of Kubernetes clusters. With Standard mode, users are responsible for managing the infrastructure and Kubernetes control plane themselves, giving them more control over the configuration and customization of their clusters.

Standard mode is priced at $0.10/hour per cluster for management fees. Users are also charged for the underlying infrastructure and any additional GCP services used in conjunction with the cluster.

GKE’s Standard mode comes with a Service Level Agreement (SLA) of 99.5% availability for the cluster control plane, ensuring high availability and reliability for mission-critical workloads.

Committed Use Discounts (CUD)

Committed Use Discounts (CUDs) are a pricing model that provides significant discounts on GCP services, including Google Kubernetes Engine, in exchange for a commitment to use the service for a certain period of time.

With CUDs, users can commit to using GKE for either one or three years and receive a discount of up to 57% or 70%, respectively, over the standard pricing. The discount applies to the management fee for the GKE cluster and is based on the level of commitment and payment frequency.

The main caveat of CUDs is that users are committing to a specific level of usage for a set period of time, which can limit their flexibility to scale up or down their usage based on changing needs.

Spot VMs

Spot VMs (formally known as Preemptible VMs) are a type of virtual machine that allows users to take advantage of unused capacity in Google’s data centers at a significantly discounted rate. They can offer savings of over 60% compared to standard VM pricing.

However, the drawback of Spot VMs is that Google can interrupt them with short notice at any time if demand for the underlying resources increases. This can potentially result in disruptions to running workloads, making Spot VMs less suitable for mission-critical or production-level workloads.

4 Ways to Optimize GKE Costs

Here are some ways to make the most cost-efficient use of GKE.

1. Ensuring the Right App Configurations

Properly configuring Kubernetes applications can help optimize the cost of running on GKE. One important step is to establish a baseline for resource utilization and performance to understand how much capacity is actually required for your applications. This can help prevent overprovisioning and unnecessary resource usage.

For Standard mode, choosing the right machine type based on your workload requirements can also help optimize costs by ensuring that you are only paying for the resources you need. In Autopilot mode, creating an accurate PodSpec definition that specifies the required resources and limits can help prevent overprovisioning and reduce costs.

2. Leveraging the Pricing Calculator

The GCP pricing calculator is a web tool that allows users to estimate the cost of running GKE clusters based on their specific usage requirements. The calculator takes into account factors such as the number of nodes, machine types, storage, and networking requirements to provide an estimate of monthly costs. This can help users plan their usage and optimize costs by adjusting the configuration and scaling of their clusters to meet their needs while minimizing costs.

3. Using Mixed Instances

A mixed-instance provisioning strategy is an approach to managing GKE clusters that uses a combination of different types of virtual machines to optimize performance and cost. With a mixed-instance provisioning strategy, GKE clusters can use a combination of different VM types with varying performance characteristics to run different workloads based on their resource requirements. This approach allows GKE to achieve high performance and availability while minimizing costs by using the most cost-effective VM type for each workload.

Cluster Autoscaler is a key tool that automatically adjusts the size of the cluster based on the workload demand. It can add or remove nodes based on workload requirements, ensuring that there is always enough capacity to meet demand without over-provisioning and incurring unnecessary costs.

4. GKE Cost Optimization Insights

Google Kubernetes Engine (GKE) cost optimization insights is a feature provided by Google Cloud Platform (GCP) that provides recommendations for optimizing your GKE infrastructure costs.

GKE cost optimization insights analyzes your GKE clusters and applications to identify potential cost savings opportunities. It provides insights and recommendations based on best practices and GCP’s experience managing large-scale Kubernetes clusters.

Ensure availability and optimize Google Kubernetes Engine with Spot by NetApp

Spot by NetApp’s portfolio provides hands-free Kubernetes optimization. It continuously analyzes how your containers are using infrastructure, automatically scaling compute resources to maximize utilization and availability utilizing the optimal blend of spot, reserved and on-demand compute instances.

  • Dramatic savings: Access spare compute capacity for up to 91% less than pay-as-you-go pricing
  • Cloud-native autoscaling: Effortlessly scale compute infrastructure for both Kubernetes and legacy workloads
  • High-availability SLA: Reliably leverage Spot VMs without disrupting your mission-critical workloads

Learn more about how Spot supports all your kubernetes workloads.