Kubernetes and Google Kubernetes Engine
Kubernetes is an open source framework for managing container based workloads and services that supports declarative setup as well as automation. It also boasts a sizable and fast expanding cross-platform integration. Kubernetes services, support, and plugin tools can also be obtained from a variety of sources.
GKE and Kubernetes are not the same thing. GKE enables fully managed Kubernetes, whereas operating Kubernetes without GKE demands human interaction, management, and monitoring to run containers and clusters.
Google Kubernetes Engine (GKE) is a centrally managed Kubernetes service that allows you to automatically execute Kubernetes workloads without having to maintain control planes, nodes, or other activities.
This article provides an in-depth explanation of the different pricing elements in GCP’s Google Kubernetes Engine, as well as a cost-optimization aligned overview.
Why use GKE?
GKE is commonly utilized by businesses and software developers while developing new applications, products, or services. Customers frequently decide to start with Kubernetes and associated docker components while creating new architecture. It is also the target infrastructure for many re-platforming efforts.
Users choose GKE as Kubernetes is challenging to set up, protect, maintain, and update on its own. The GKE solution takes care of all of these obstacles, allowing businesses to focus on providing services rather than constantly monitoring their infrastructure.
Google Kubernetes Engine (GKE) Pricing
GKE pricing is mostly dependent on the mode selected by the user (Standard or Autopilot). Standard is charged based on the machine configuration and is subject to Compute Engine price. Autopilot features a more appropriate price structure for those who are uninterested with technological complexities.
Cluster Management & Free Tier
- $0.10 per cluster per hour (paid in 1 second increments) cluster administration cost applies to all GKE clusters, regardless of mode of operation, cluster size, or topology.
- GKE’s free tier offers $74.40 in monthly credits per billing account, which may be applied to zonal and Autopilot clusters. If you only use one Zonal or Autopilot cluster each month, this credit will at least cover the whole cost of that cluster.
- Unused free tier credits are not carried over and cannot be used to purchase further SKUs (for example, they cannot be applied to compute charges, or the cluster fee for Regional clusters).
Standard Mode Pricing
Users can configure clusters and the infrastructure supporting them in Standard Mode. While users have the greatest degree of freedom, cluster management is their responsibility.
Considering standard mode makes use of Compute Engine instances as worker nodes, billing is mostly determined by Compute Engine pricing. Usage based pricing is the default, with instances are billed per second until the nodes are deleted. Users can save money by taking advantage of committed-use discounts, signing a one-year or three-year contract, or participating in spot occurrences. Pricing is based on the virtual machine instance/service applied.
- Committed use discounts offer customers and businesses substantial savings when they commit to a one- or three-year plan.
- Expect reductions of up to 70% on a three-year commitment and as little as 20% on a one-year commitment. The price is consistent across regions.
- Obtain access to basic computing engine resources (GPUs, local SSDs, and others) in addition to access to automated and standard clusters, allowing customers to save even more money.
If your containers and clusters demand predictable resources with minimal to no downtime, a committed usage pricing plan is most likely the ideal solution for you.
Automatic Mode Pricing
Cluster administration is completely handled in Autopilot mode, which allows for easy and efficient ways to deploy and operate clusters. However, in terms of the foundational technical infrastructure, this approach offers minimal flexibility.
Autopilot is a pay-per-pod service. Users are charged according on the CPU, memory, and temporal storage needs of the pods. Users can choose which Computing Class to employ for their workloads.
Autopilot pods have three compute classes:
The default class is general-purpose, which is best suited for medium-intensity workloads such as web servers, small to medium-sized databases, or application frameworks. If you do not include a specific compute class in your Pod specification, Autopilot will assume the general-purpose compute class for you.
The balanced compute class is intended for CPU or memory requirements that exceed the general-purpose compute class maximums. It is ideal for caching, multimedia streaming, and and running large processor workloads.
The Scale-out class disables simultaneous multi-threading and are scale-optimized. They are ideally suited to high-volume workloads such as containerized microservices, data log processing, and large-scale Java applications.
Discounts On Spot VMs
Spot VMs are preemptible virtual machines that are discounted by Google Cloud. Since spot VMs are basically surplus Compute Engine capacity, their availability changes according to Compute Engine utilization. Spot VMs have no minimum or maximum runtime unless you explicitly limit it.
- The only drawback is that they are subject to interruptions at any moment, with just a 30 seconds warning. As a result, while you may utilize these virtual machine instances and resources for GKE, your apps and containers may experience disruptions.
- Spot VMs are substantially cheaper than ordinary VMs on-demand, with reductions ranging from 60-91% for machine types and GPUs, as well as lesser discounts for local SSDs. The reductions are substantial, especially when compared to regular pay-as-you-go rates.
Spot VMs may not be the greatest solution if you’re hosting a client-facing application container. However, if you’re utilizing these containers for backups or less critical work (that may be paused), this strategy can help you save a lot of resources on your cloud expenditure.
Multi Cluster Ingress
Multi Cluster Ingress is a Google Kubernetes Engine (GKE) cloud-based controller. It is a Google-hosted service that enables the deployment of common load balancing assets across clusters and regions. Multi Cluster Ingress is intended to address the load balancing requirements of multi-cluster, multi-regional settings.
Anthos is a Google Cloud service that aggregates various services, both internal and external to Google Cloud. Multi Cluster Ingress is billed under Anthos only if the Anthos API is enabled.
When using Multi Cluster Ingress, you will be charged at the standalone price rate if your GKE clusters are not licensed for Anthos. Load balancers and bandwidth for Multi Cluster Ingress resources are charged individually in all circumstances, according to load balancer pricing.
Backup for GKE is a Google service that can be utilized to safeguard and maintain GKE data. Workload backups may be beneficial for disaster recovery, CI/CD pipelines, replicating workloads, or upgrade circumstances. Workload protection can also assist you in achieving important data-recovery-time objectives.
GKE backup is priced based on two dimensions, both prices are billed monthly, much like other GKE feature invoicing.
- Backup management charges for GKE is calculated depending on the number of GKE pods protected.
- Backup storage charges are calculated based on the amount of data (GB) saved.
You may back up or restore specific or all workloads. Workloads from one cluster can be backed up and restored to another. You may plan backups to occur automatically so that you can restore your workloads quickly in the case of an issue. Both prices are charged monthly, much like other GKE feature payments.
Using Pricing Calculator
There is no right or incorrect GKE pricing strategy for your company. Instead, you must examine your Google Kubernetes requirements independently and select the optimal plan for your workload. GCP provides a very easy and efficient method for understanding pricing in Google Cloud. The GCP Pricing Calculator is a web-based application that may be used to estimate the usage of specific services and resources.
After entering the necessary parameters regarding GCP Services (instances, provisioning, OS, Machine type, and so on), the tool conducts a simulation and presents you with an estimated cost. You may change the currency and time range to suit your needs, and when you’re complete, you can email or save the quote to a specific URL for future analysis.
Visit the Pricing Calculator page to get a quote on your projected instance usages.
Pricing for GCP’s Google Kubernetes Engine (GKE) is divided into two categories: Standard and Autopilot.
Users can set their underlying technical infrastructure with Standard, and invoicing is dependent on machine types chosen from the worker nodes. Autopilot is a completely managed service in which users simply need to set pod specs and are not bothered with any other technological concerns. GKE Autopilot pricing is based on the amount of vCPU and RAM required.
Multi Cluster Ingress is designed to meet the load balancing needs of multi-cluster, multi-regional environments. Backup for GKE is another Google service that may be used to protect and preserve GKE data.
An important takeaway: Creating and following a FinOps strategy will enable you to effectively utilize several GKE capabilities including autoscaling, load balancing, monitoring, and other tools to contribute towards GKE cost optimization.