Suppose you’re thinking about getting started with Kubernetes or using it as the basis to deploy apps reliably across the cloud. In that case, you’re probably looking at the top two cloud providers and their Kubernetes services – Google Kubernetes Engine(GKE) and Azure Kubernetes Engine(AKE). But which one of these two is a good choice? Furthermore, what are the differences between GKE and AKS? Here in this article, we will learn about the same.
What is Google Kubernetes Service(GKE)?
GKE or Google Kubernetes Engine is a platform to deploy, manage and scale containerized applications on Google infrastructure. The GKE environment comprises numerous compute Engine Instances clustered together, and the Kubernetes open-source cluster management drives it. You deploy and manage containerized apps, execute administrative activities, define policies, and monitor the health using Kubernetes commands and resources.
GKE is designed for containerized apps that have been packed into insolated and platform-independent user-space instances, such as by utilizing Docker. These containers are referred to as workloads in GKE. Before deploying a workload on a GKE cluster, it must first be packaged into a container.
Compared to other Kubernetes service providers like EKS and AKS, Google Kubernetes Engine(GKE) is the most robust and well-rounded Kubernetes solution. It has the greatest uptime SLA. In addition, it supports the Istio service mesh and gvisor for additional security between running containers.
GKE clusters can operate in one of two modes:
Gives you complete control over your clusters and node architecture, and node configuration flexibility. For clusters established in Standard mode, you choose the specifications required for workloads and pay only for the nodes you utilize.
Takes care of the complete cluster and node infrastructure. Autopilot delivers a hands-off Kubernetes experience, allowing you to concentrate on your workloads while only paying for the resources needed to operate your applications. In addition, autopilot clusters have an optimized cluster configuration, ready for production workloads.
Advantages of using GKE
Kubernetes is a native application for GKE
Consider K8s and GKE to be that pair that have been together since the first year of college. Because Kubernetes has been working with GKE since the beginning, specific tasks are straightforward. For instance, K8s has new versions every couple of months; if you’ve ever had to update it on your own, you know how time-consuming it is to spin up a new cluster, move your pods to a new cluster, and so on. Isn’t it frustrating? In GKE, with a single command, everything is immediately updated.
Docker and GKE Combination
Docker and GKE work together well. GKE supports the Docker container format and makes it easy to store and retrieve your own Docker images. On the other hand, Google Kubernetes Engine automatically schedules and maintains your containers based on CPU and memory specifications. Furthermore, because it is based on Kubernetes, you can use public cloud, hybrid, and on-premises infrastructure.
GKE works on a pay-as-you-go pricing model. You get everything based on your needs to deploy containerized apps, and you pay for only what you use. As a result, it is the most straightforward and affordable offering for your Docker needs, with no upfront payments and no termination penalties.
What is Azure Kubernetes Service(AKS)?
AKS is an abbreviation for Azure Kubernetes Service, and it is a managed container orchestration service built on the open-source K8s architecture and accessible through Microsoft Azure. An enterprise may use AKS to control essential functions such as deploying, scaling, and maintaining container-based applications.
You get serverless Kubernetes, robust security, and CI/CD integration with AKS. It features controlled worker nodes and is extensively linked with the rest of Microsoft’s cloud services. It is unquestionably best-of-breed for seamless interaction with Microsoft’s cross-platform development tools, including DevOps and VS Code.
AKS enables you to deploy, manage and upgrade resources in the K8s cluster without downtime. The most significant part is that you don’t need extensive expertise in container orchestration to handle AKS. Instead, you can build and manage AKS clusters using the Azure portal and Azure CLI.
You can also choose a template-based deployment using Resource Manager Templates and Terraform to install the AKS cluster, which controls the auto-configuration of the Kubernetes cluster’s master and worker nodes. The user defines the number and size of nodes, and the AKS configures secure communication between the control plane and nodes. AKS clusters must have at least one node, and nodes with similar configurations are combined to form node pools.
Advantages of using AKS
Development tool integration
Tools such as Draft and Helm are easily integrated with AKS, allowing developers to develop faster and more iterative Kubernetes. In addition, containers may be operated and debugged directly in the Azure Kubernetes environment, reducing setup load.
AKS also supports the Docker image format and can work with Azure Container Registry to enable private storage for Docker images. Furthermore, consistent compliance with industry standards such as SOC, PCI DSS, HIPAA, and ISO makes AKS more dependable across many businesses.
Role-based access control
You can connect Azure Active Directory with Azure Kubernetes Service to enable security with role-based access control. You may also keep track of how your AKS and applications are performing.
Enterprise commitment in an open-source ecosystem
Microsoft continuously works to make Kubernetes development with Azure easier. Also, they are encouraging developers to use and participate in open-source projects. As a result, Microsoft is the third major contributor to making K8s cloud-native and business-oriented.
AKS offers cluster monitoring, management, auto-upgrades, and scaling, reducing maintenance and faster development. It also allows for the instant deployment of extra computing resources in Serverless Kubernetes without managing the Kubernetes infrastructure.
Difference between GKE and AKS
Azure Kubernetes Service supports small changes and upgrades in an organized manner, which keeps customers engaged with the most updated version of Kubernetes. Although the overall procedure involves some manual work, AKS provides automatic node updates.
In addition, AKS has node auto-repair capabilities, which may be utilized with auto-scaling node pools. As a result, AKS guarantees 99.95 percent uptime. GKE supports numerous current Kubernetes versions—typically 12 primary and four minor versions. GKE provides automatic worker node and control plane updates.
Also, it includes the detection and treatment of unhealthy nodes. GKE includes auto-repair by default, which allows for automatic cluster health maintenance. GKE provides a 99.95 percent uptime SLA.
Two Microsoft Azure options are available for AKS monitoring. Azure Monitor can assist you in determining the health of containers. You may use Application Insights to monitor Kubernetes components and also set up Istio, a service mesh framework, to do so. GKE monitoring may be set up using Stackdriver. Monitoring is provided for both workers and master nodes.
AKS allows you to use VS Code to access Kubernetes extensions. Bridge to Kubernetes enables you to run local code as a service within a cluster. GKE includes Cloud Code, which functions like a VS code or IntelliJ plugin. It also allows you to deploy, administrate, and debug your cluster directly from your code editor.
GKE and AKS offer different tools and technologies with robust security features. For example, role-based access control is enabled in Google Kubernetes Engine and Azure Kubernetes Engine. Calico may be used to configure network policies on both platforms. In addition, AKS provides network policy assistance.
Encryption: AKS uses Azure KMS for encryption. You can have Azure handle your encryption keys or keep customer-managed keys. With Cloud KMS, GKE provides data-at-rest encryption.
AKS provides serverless computing via virtual nodes. This feature allows you to run Kubernetes pods with Azure Container Instances rather than entire virtual machines. This allows for faster scaling. Target specific workloads to run on virtual nodes and add more nodes as required. In addition, GKE’s Cloud Run for Anthos capability provides serverless capabilities.
Cloud Run is a managed serverless container platform that provides exceptionally flexible deployments that may scale to zero utilizing per-request scaling, enabling you to select whether to use self-managed GCP VMs or Cloud Run resources.
AKS and GKE claim to be the most satisfactory Managed Kubernetes solution on the cloud. However, it is entirely on you to choose the one for your organization, whether you want to take advantage of Google’s most mature and cost-effective solution or use your Microsoft Enterprise Agreement to obtain better price and support on Azure.
That’s it for this tutorial.
Amit Doshi is a Cloud Engineer who has experienced more than 5 years in AWS, Azure, and Google Cloud. He is an IT professional responsible for designing, implementing, managing, and maintaining cloud computing infrastructure, applications, and services.