AWS Knowledge
Understanding Google Kubernetes Engine Pricing
Piyush Kalra
Nov 30, 2024
Hands down, Google Kubernetes Engine is one of the best-managed service platforms for deploying, managing, and scaling containerized apps. It’s part of the Google Cloud and stems out as one of the most suitable solutions for enterprises that require the use of Kubernetes without the complexity of managing infrastructure.
But to truly take full advantage of GKE, one must be well-versed in its pricing structure. Whether you’re an independent programmer or working with DevOps teams, knowing GKE’s pricing intricacies can be useful in managing mechanics and also making sure there are no “wait, what?” moments in the bill when it comes to cloud services.
Let’s get right down to it: GKE’s pricing policies have often been depicted in long, ambiguous documents, which leads to overspending; however, this post will work towards the elimination of such a case. We’d love to pay less for GKE’s services. Did you know your GKE configuration can save you over 30% more? With the help of this guide, it will become much clearer how to receive the maximum number of GKE services while keeping the costs reasonable.
What is Google Kubernetes Engine (GKE)?
Google Kubernetes Engine is a managed service that automates deployment, scaling, and management of containerized applications. Google Cloud-trained cluster management, integrated logging, and cost administration capabilities amplify Kubernetes’ initial performance.
Key Features of GKE:
Cluster Management: It is easy to create, operate and maintain Kubernetes clusters. GKE handles patching, upgrades, maintenance, and other elements affecting the operation.
Autoscaling: Horizontal and Vertical Pod Autoscalers increase or decrease the application's resource sizes.
Integration with Google Cloud: Many of GKE’s integrations are with Google Cloud offerings, including BigQuery, Cloud Storage and Compute Engine.
Security features: Numerous levels of security features are offered, such as built-in security features, workload identity, and integration with Google Cloud IAM.
How Does Google Kubernetes Engine Work?
Automation of actions accompanying GKE planning facilitates and makes the tasks that optimizing resources requires easier:
Nodes and Clusters: Resources are put into clusters, which are composed of nodes where your application workloads run. Nodes are Virtual machines that serve as the hosts for your containers.
Control Plane: Scheduling, reallocating Kubernetes workloads, and ensuring their completion status and maintenance are GKE’s prime functioning tasks and are executed from a control plane that is both protected and trustworthy.
Containerized Applications: The way to deploy applications is by using containers, which can be viewed as wrappers of the necessary software needed for an application to function correctly and in various environments.
Deep Dive into Google Kubernetes Engine Pricing Structure
GKE's pricing model includes several components—some standard and others based on optional features. Understanding these can help you select the most cost-effective setup.
Pricing Models
Standard Mode
Standard Mode offers full control over infrastructure:
Cluster Management Fee: $0.10 per cluster/hour (charged in 1-second increments). This fee applies to all, regardless of cluster size, location, or tiers.
Node Pricing: Includes the cost of Compute Engine instances (worker nodes) used in your cluster. Charges are based on Google Cloud’s Compute Engine pricing with committed use discounts available for long-term users.
Enterprise Mode
Enterprise Mode offers all standard features, multi-team operations, advanced security, service mesh, configuration management, observability metrics, and a unified console.
Cluster Management Fee: $0.00822 per vCPU per hour or $6 per vCPU per month.
Resource Pricing: Includes Compute Engine, networking, and multi-cloud features. Charges vary by usage and environment. On-premises options exclude admin clusters and control plane nodes from vCPU billing.
Autopilot Mode
Autopilot mode automatically provisions and manages resources for you, offering a more hands-off experience.
Cluster Management Fee: $0.10 per cluster/hour, similar to Standard Mode.
Pay-per-Pod Pricing: With Autopilot, you’re billed for the CPU, memory, and ephemeral storage your pods request, not the underlying nodes. Billing occurs in 1-second increments.
GKE's Free Tier
Google Kubernetes Engine offers free credits worth $74.40 monthly to users with zonal or Autopilot clusters. This makes it free to maintain a single zonal or Autopilot cluster. Unfortunately, unused credits don’t carry over to the next month.
For example, let’s say you use:
Autopilot cluster for 1,000 hours/month
Regional cluster for 1,000 hours/month
Zonal cluster for 1000 hours/month
In total, you would use the clusters for 3000 hours, and this would bring your cost to $300, with the clusters being priced at $0.10 per hour. Accounting for the free credits worth $74.40, the new billing amount shifts from $300 to $225.60.
Key Components of GKE Pricing
A few factors, as listed below, determine the price for GKE. Here’s a breakdown of the main components to help you understand how costs are calculated:
Node Costs
GKE Gives the option to choose a variety of VMs, which include but aren’t limited to high-cpu and general-purpose.
In the case of non-critical workloads or batch workloads, Spot VMs are a more affordable option, even though they can be preempted at any point in time.
Control Plane Costs
In Standard Mode, a flat rate of $0.10 is charged every hour for every cluster control plane that handles the management operations of your cluster.
In Autopilot Mode, the control plane fee is absorbed in the ‘all in one’ pricing model, which facilitates cost management ease.
Network and Storage Costs
Network Egress: All the outbound data transfer from Google Cloud to the Internet-bound or inter-region traffic is chargeable.
Persistent Disks: If your pods demand a disk, then the type and dimension of the disk will be charged. GKE has a wide selection of disk storage options suitable for all performance requirements.
Additional Services
Applications such as Backup for GKE are billed separately, with the lowest costs starting at $0.00171/hr per pod. If the commitment is for a period of 3 years, the rates drop to $0.00094/hr per pod, which is more economical, though the hourly rates for storage charge start from $0.0000479/hr per gigabyte.
Note: The rates may be different based on the region of usage.
Sample Cost Calculation Explained
Let’s break down the costs for running an Autopilot cluster with two pods, each requesting 1 vCPU and 4 GB of memory for a 31-day month:
Cluster Management Fee: Chargeable at the rate of $0.10 per hour, the cluster management cost can be calculated by considering that there are 24 hours in a day for a total of 31 days. Thus, the total cost would be:
$0.10 × 24 hours × 31 days = $74.40
Note: This fee is covered by the free tier.
Pod Costs: In terms of hourly rates, per pod that needs 1 vCPU and 4 GB of RAM, the costs are:
1 vCPU costs $0.0582 per hour.
4 GB of RAM costs $0.0064 per GB per hour (4 GB = $0.0064 × 4 = $0.0256 per hour).
Thus, the total costs for a pod per hour comes to:
$0.0582 (vCPU) + $0.0256 (RAM) = $0.0838/hour
For 2 pods running 24 hours a day for 31 days, the cost is:
2 pods × $0.0838/hour × 24 hours × 31 days = ~$253.12
Furthermore, you can use the Google Cloud pricing calculator to better tailor estimates for your specific setups.
Case Study: How Omneo Optimized Customer Experiences with Kubernetes
Omneo employs a data-driven customer/service retention platform, but they rely on obsolete architecture for their customers and services. Real-time events got slowed during heavy traffic, limiting insights and customer engagement. There was a tendency to focus more time on infrastructure rather than feature development by the developers, and there was further pressure with increased hosting fees.
Solution
Omneo made use of Google Kubernetes Engine to create a scalable microservices-based architecture. These containerized services saved 40% of hosting services, and Terraform cut down on server commissioning, which lowered the developer infrastructure time requirement by 70%. The automatic resizing of GKE improved the times that features were deployed, and the high availability of real-time data to the customers was further improved by combining Cloud SQL with Memorystore, enabling greater responsiveness during busy periods.
Results
70% Increase in Developer Efficiency: Resulting in factors such as an increase in new and innovative features.
40% Reduction in Cost: Resulting in greater cost optimization of resource utilization through Kubernetes.
A larger number of monthly updates: These include hundreds each month.
Growth in Customer Participation: Use of real-time data led to better decision-making and processing of more timely insights.
Simple Tips to Save Money on GKE
Even if it feels challenging at first to lower the costs for the Google Kubernetes Engine, such an endeavour is possible with the right means at one’s disposal. Here are a few ways that can help:
Keep an Eye on Your Usage: Use the Google Cloud Console to monitor your GKE setup. The dashboards give you a clear picture of how resources are used so you can quickly spot and fix inefficiencies.
Set the Right Resource Limits: As with everything else, there are always limits; make them tighter when using GKE to secure destruction, as GKE allows you to restrict the amount of CPU or memory one pod can request.
Use Autoscaling for Flexibility:
Horizontal Pod Autoscaler: It automatically changes the number of pods active during traffic spikes to improve performance and ensure smooth operations.
Cluster Autoscaler: Resizes infrastructure resources when traffic is usually low to optimise costs.
Choose Cost-Friendly Machines: For non-critical workloads, E2 machine types or Spot VMs are vastly more affordable and will enable your organization to save a large amount of cash.
Take Advantage of Discounts: If your workloads follow a consistent and predictable format, signing up for committed use discounts is highly recommended.
Conclusion
Understanding the competitive Google Kubernetes Engine pricing is fundamental in determining your respective projects' performance vs cost ratio. Be it the Standard Mode, which provides more flexibility and control or the Autopilot Mode, which takes away the hassle of dealing with infrastructure, GKE allows developers to concentrate on building great applications, not infrastructure. It’s a great solution that can grow with your needs, enabling you to scale and work efficiently.
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