How Is Google Kubernetes Engine (GKE) Priced?
Google Kubernetes Engine (GKE) was the world’s first managed Kubernetes service, and is still one of the most popular. Its pricing is based on various factors, including a cluster management fee, the cost of Google Cloud virtual machines (VMs) for nodes, persistent storage, and network resources.
GKE offers several pricing tiers and models:
- Free Tier: This allows limited usage of nodes and storage for free, mainly aimed at small-scale projects or testing environments.
- Standard Mode: This is pay-as-you-go pricing, where you’re billed for the resources like CPU, memory, and storage that you actually use.
- Enterprise Edition: This tier offers advanced features at an additional per-node fee, useful for organizations with complex operational needs.
- Autopilot Mode: Here, you pay based on actual resources used by your Kubernetes pods, such as CPU and memory.
- Committed Use Discounts (CUD): You commit to a specific amount of resources for a one or three-year term, and in return, you get discounted pricing.
- Spot VMs: These offer lower costs by letting you use excess capacity in Google Cloud, but they can be terminated if the resources are needed elsewhere.
This is part of a series of articles about Kubernetes costs.
In this article:
- Core Components Affecting GKE Pricing
- Google Kubernetes Engine Pricing Models with Examples
- Best Practices for Optimizing GKE Costs
- Autonomous Kubernetes Optimization Solutions
Core Components Affecting GKE Pricing
Cluster Management Fee
GKE charges a cluster management fee of $0.10 / hour / cluster, covering the management of the Kubernetes control plane, including the master node and its upgrades. This fee is applied per cluster, not per node. As you scale your operations and add more nodes, the management fee remains constant. However, the fee can add up if you run multiple clusters.
Node pricing is perhaps the most variable component of GKE’s cost structure. This cost depends on the type of machines you choose to run your workloads and the regions where these machines are located. Google offers a wide range of machine types, each with its own price point. Learn more in Google Cloud’s VM pricing page.
Note that in GKE Autopilot Mode you do not pay directly for nodes. Instead, you are billed for the specific computing resources used by your Kubernetes pods.
Persistent storage is another key factor that affects the total cost of using GKE. Depending on your application’s requirements, you may need to use persistent disks or the Google Filestore service. The cost of these storage options varies based on the type of storage (SSD or HDD), the size, and the region.
Networking costs in GKE are primarily based on the amount of data sent and received outside of Google’s network. This might include costs for load balancing, network traffic, and data egress. The exact pricing of networking will depend on the Google Cloud network products you use for your GKE cluster. Refer to Google Cloud’s network pricing page for more details.
Google Kubernetes Engine Pricing Models with Examples
In the section below we provide some pricing examples, to help you get a general idea of GKE costs. Please note that cloud pricing is subject to change. For up-to-date pricing information consult Google Cloud’s official pricing page.
Google offers a generous free tier for GKE, allowing users to get started with the platform at no cost. With the free tier, you get $74.40 in monthly credits per billing account that are applied to zonal and Autopilot clusters. This is perfect for small-scale development and testing environments.
However, it’s important to note that you are still charged $0.10 per hour for cluster management, and that any usage beyond the free tier will be billed according to the standard pricing models.
The standard mode is the default pricing model for GKE. With this mode, you pay for the resources you use, including node instances, persistent disk storage, and networking. Node instances are billed based on their CPU and memory resources, while persistent disk storage is billed based on the amount of data stored. Networking costs are based on the amount of data transferred between nodes, clusters, and external services.
To illustrate the pricing, let’s consider an example in the US East 1 region. Suppose you have a GKE cluster with two n1-standard-2 nodes and 100GB of persistent disk storage. The monthly cost for compute would be $78, and the cost for persistent disk storage would be $4. In addition, you’d pay $0.10 per hour, or $73 per month, for cluster management.
For organizations with more complex requirements, Google offers the Enterprise Edition of GKE. This edition provides additional features such as advanced security controls, enhanced support, and integration with other Google Cloud services. The pricing for the Enterprise Edition is based on a per-node fee, which includes the cost of node instances, persistent disk storage, and networking.
Let’s continue with our previous example. Each n1-standard-2 node has two vCPUs, and two nodes have four vCPUs total. The enterprise edition is priced at $0.0083 per vCPU hour, so running both nodes for 730 hours (one month) would cost $78 for compute, $4 for persistent storage, $73 for cluster management, and an additional $24 for enterprise-tier GKE management.
Autopilot Mode is a new feature introduced by Google Kubernetes Engine. It is designed to simplify the management and operation of Kubernetes clusters by automating many tasks that were previously performed by administrators. Autopilot Mode offers a fully managed experience, where Google takes care of the underlying infrastructure, including node provisioning, scaling, and maintenance.
Autopilot Mode pricing is based on the computing resources consumed by your Kubernetes pods. You can run general-purpose pods, scale-out pods, balanced compute pods, or GPU pods. Each of these has separate pricing for vCPUs, memory, and GPUs (if applicable).
Let’s consider an example. Suppose you run a general purpose pod, in the North Virginia region, that requires 2 vCPUs and 4 GB of memory. The monthly cost will be $73.146 for the CPU resources and $16.189 for the memory, in total $89 to run the pod for one consecutive month.
Committed Use Discounts
Committed Use Discounts (CUD) is another pricing option available for Google Kubernetes Engine. It allows you to commit to using a certain amount of resources, such as CPU cores or memory, for a one or three-year term. In return, you receive a significant discount on the regular on-demand pricing.
CUD offers a discount of up to 57% for most resources like machine types or GPUs, and up to 70% for memory-optimized machine types. The exact discount percentage depends on the term (one or three years) and the committed usage. Learn more in the official Google Cloud committed discounts page.
Spot VMs are a cost-effective option for running workloads that can tolerate interruptions. They are virtual machines that are priced based on the available excess capacity in the Google Cloud Platform. Spot VMs can be used in Google Kubernetes Engine to reduce costs, but keep in mind that these VMs can be interrupted at only a few seconds’ notice.
By using Spot VMs, you can reduce the cost of running your workloads by up to 90% compared to the regular on-demand pricing. The exact savings depend on the availability of excess capacity in the Google Cloud Platform. Learn more in Google Cloud’s official page.
Best Practices for Optimizing GKE Costs
Here are a few ways you can optimize and reduce your costs when using Google Kubrnetes Engine.
Using GKE Pricing Calculator
A valuable tool in your quest to understand and optimize GKE pricing is the GKE Pricing Calculator. This online tool allows you to estimate the cost of running your specific workloads on GKE. You can input various parameters, such as the number of nodes, the type of nodes, and the amount of storage, and the calculator will provide you with an estimated monthly cost.
The GKE Pricing Calculator can help you experiment with different configurations and understand the cost implications of each. By simulating different scenarios, you can identify the most cost-effective configuration for your workloads and effectively plan your budget.
Rightsize Cluster Resources
One of the most practical ways to optimize your GKE costs is to rightsize your cluster resources. By rightsizing, you ensure that you’re only paying for the compute resources that your applications genuinely need.
Take advantage of Kubernetes’ built-in resource requests and limits. Resource requests ensure that each pod in your cluster gets the CPU and memory it needs, while limits prevent pods from using more than a specified amount of resources. By effectively using these settings, you can efficiently allocate resources and avoid overspending.
Please note that by using GKE Autopilot mode, you automatically pay only for the resources your pods use, eliminating the need for resizing.
Utilize Cluster Autoscaling
GKE’s cluster autoscaler automatically adjusts the size of your cluster based on the demands of your workloads. When there are pods that can’t be scheduled due to resource constraints, the cluster autoscaler adds more nodes. Conversely, if nodes are underutilized and all pods could be scheduled even with fewer nodes, it removes nodes to save costs.
However, it’s important to note that while autoscaling can reduce costs by optimizing resource utilization, it also requires careful management. You need to ensure that you have set up proper autoscaling policies and that you monitor your cluster’s performance regularly to avoid unexpected cost surges or insufficient resources for your workloads.
Optimize Storage Costs
Storage is a significant factor in the total cost of running a GKE cluster. Therefore, optimizing storage costs is an essential part of managing your GKE expenses. Kubernetes offers various types of storage options, including persistent volumes (PVs), persistent volume claims (PVCs), and storage classes.
By understanding these storage options and their pricing, you can choose the most cost-effective solution for your needs. Additionally, you can take advantage of features such as volume snapshotting and dynamic volume provisioning to further control your storage costs.
Leverage Workload Identity
Workload Identity is a feature of GKE that allows you to bind Kubernetes service accounts to Google Cloud service accounts. This binding allows your workloads to authenticate to other Google Cloud services without the need to manage individual service account keys, reducing the risk of key leakage and unauthorized access.
Workload Identity can impact GKE costs because it ensures only authorized individuals can make changes to your clusters. This means that each team can be held accountable for the costs incurred by the Kubernetes clusters it operates. This is crucial for effective governance of your GKE deployments.
Regularly cleaning up your GKE environment is another effective way to control your costs. Over time, it’s easy to accumulate unused resources, such as idle persistent disks, unused IP addresses, and unnecessary snapshots. These resources may seem insignificant, but they can add up to substantial costs over time.
Set up a regular schedule to review and clean up your GKE environment. Remove any resources that are no longer needed and ensure that all your configurations are optimized for cost efficiency. Regular cleanup activities can help you maintain a lean, cost-effective GKE environment.
Autonomous Kubernetes Optimization Solutions
For the next level of optimizing Kubernetes workloads, there are autonomous, continuous solutions that can reduce costs without affecting stability. Intel Granulate continuously and autonomously optimizes containerized environments for improved application performance.
With Intel Granulate’s Capacity Optimization, DevOps teams can automatically apply pod resource rightsizing recommendations to their Kubernetes environments, while simultaneously optimizing the application on the runtime layer.
By continuously adapting resources and runtime environments to application workload patterns, teams can avoid constant monitoring and benchmarking, tuning workload resource capacity specifically for their unique workloads.