Comparing the K8 offerings of the EKS, AKS and GCP

Kubernetes enables the automated orchestration of containerized workloads by abstracting machine resources for unified consumption by cluster objects. The platform, therefore, allows enterprises to build microservice-based, cloud-native applications.

With the rising popularity of Kubernetes, major cloud platforms now offer managed Kubernetes services to simplify container orchestration. These environments include Amazon Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), and Google Kubernetes Engine (GKE). This article compares the top three managed Kubernetes cloud services.

Overview of Amazon EKS

The Elastic Kubernetes Service is an AWS PaaS offering that enables the management and orchestration of containerized applications in a Kubernetes-based deployment. The platform automates cluster creation, application deployment, and workload scaling, simplifying Kubernetes applications’ management by leveraging AWS infrastructure and services.

EKS features

Key features of the EKS service include the following:

Managed node groups

EKS enables managed node groups so cluster administrators don’t have to provision or register the worker nodes needed to run containerized applications. EKS manages every node provisioned within an autoscaling group to automate the creation, updation, and termination of EC2 instances, depending on the application’s lifecycle.

Cluster access points

When the API server’s endpoint is open to the public internet, administrators manage access to the cluster using RBAC and IAM policies. Administrators can also limit cluster access by isolating the cluster’s VPC, allowing only exclusive internal access. EKS clusters can, therefore, have both public and private access points.

AWS Outposts

AWS Outposts allows organizations to run AWS infrastructure and services on-prem to reduce latency and lower costs. Administrators can, as a result, leverage Outposts to deploy EKS nodes in on-prem environments to orchestrate hybrid clusters.

Fargate integration for serverless provisioning

EKS can deploy Kubernetes workloads on the AWS Fargate serverless service to offer unlimited compute resources. Fargate provides pre-configured compute sizes to right-size worker nodes, eliminating the overhead associated with application patches and upgrades.

EKS pros

Some benefits of using EKS for Kubernetes workloads include:

Granular access control

EKS implements AWS IAM policies for Kubernetes RBAC so administrators can use IAM policies to define roles and permissions for all entities in the cluster. Applying IAM policies to service accounts enforces access policies at pod-level.

Serverless provisioning

Running workloads using Fargate profiles eliminates the need to provision more VMs, as the service automatically creates worker nodes based on preconfigured server instances. As the serverless platform offers unlimited compute resources, these server instances can be of any size and amount.

Out-of-the-box on-prem integration

Infrastructure teams can connect containerized workloads on-prem using EKS Anywhere. Cluster administrators can also use AWS Outposts to create and run EKS nodes on-prem for lower latency, data residency, and local data processing needs.

EKS cons

The main drawback of running Kubernetes workloads on EKS is its cost . Amazon charges $0.10 per hour for each EKS cluster, regardless of workload size. Clients have to pay for other AWS services provisioned to the cluster, including EC2 instances, bucket storage, and networks, which all add up to the total costs of ownership.

An overview of Azure AKS

The Azure Kubernetes Service (AKS) leverages Azure’s infrastructure and built-in CI/CD capabilities to enable automated container orchestration. AKS can not only be used to deploy Kubernetes workloads on Azure Virtual Machines but also allows for on-prem, multi-cloud, and hybrid orchestration with Azure Arc. By tying together Kubernetes, GitOps, and DevOps best practices, AKS allows for efficient cluster resource utilization while easing stress on developers and administrators.

AKS features

Key features of AKS include:

Azure policies for pod-level security

The Azure pod security policy add-on creates gatekeeper components that implement the Open Policy Agent for workloads at pod level. This allows administrators to define and govern the compliance state of each service running within the cluster.

Azure pipelines for CI/CD

Software teams can implement CI/CD by integrating AKS, the Azure Container Registry, Cosmos DB, and other Azure services. This enables secure testing, integration, and deployment for quicker release cycles.

Access control with Azure Access Directory

AKS allows Kubernetes RBAC role binding with Azure Active Directory (AD). Administrators can define permissions and privileges for Kubernetes entities based on Azure AD users and groups.

AKS pros

Some benefits of using AKS for Kubernetes workloads include:

Improved productivity

The AKS service also creates, scales, and terminates worker nodes on demand, reducing the stress on developers and cluster administrators.

Built-in high availability

AKS uses virtual machine scale sets (VMSSs) to ensure graceful node shutdown and high availability during node failures. Clusters can be replicated in multiple Azure regions then paired for resilience and disaster recovery.

AKS cons

The main drawback of using AKS for Kubernetes workloads is that the service requires manual updates when moving to newer versions of Kubernetes. This may lead to compatibility and availability failures if the updates are not scheduled properly.

An overview of Google GKE

Google Kubernetes Engine enables the deployment and management of containerized workloads using Google Cloud Infrastructure. GKE enjoys tight integrations with Kubernetes, since they were both formulated and developed at Google, allowing for early access to Kubernetes features and upgrades.

GKE features

Salient features of the GKE service include the following:

Standard and autopilot operation modes

GKE provides two modes of operation depending on the level of control, responsibility, and flexibility desired by cluster administrators. The standard mode allows administrators to provision and manage cluster nodes, enforcing more control and flexibility. The autopilot mode manages cluster infrastructure and operations, providing a hands-off experience.

Pre-built application templates

Administrators can install production-ready, commercial-grade apps from the Google Cloud marketplace to accelerate the deployment of such functionalities as access control, licensing, and networking.

Four-way autoscaling

GKE performs horizontal autoscaling by adding or removing nodes based on changes in workload resource requirements. The service also adjusts the type, CPU, and memory of VMs running workloads when pods require vertical autoscaling.

GKE pros

Some advantages of running containerized workloads on GKE clusters include:

Early access to K8s updates

The development and evolution of GKE is guided by the principles, processes, and practices used to build Kubernetes. This innate integration with the codebase provides GKE clusters with privileged early access to the latest Kubernetes upgrades and features.

Low ownership and maintenance costs

GKE includes a free-tier cluster (a standard mode cluster with fewer than three nodes). Larger standard clusters start at $0.10 per hour, lowering total ownership costs. Considering autopilot clusters are fully managed, services are reasonably priced that begin at $0.10 per hour.

Release channels streamline operations

GKE uses release channels to automate the uptake of Kubernetes upgrades depending on the required level of feature availability and stability. GKE supports three release channels, with the Rapid channel incorporating features as soon as they reach GA status and the Stable channel incorporating features up to 6 months after release.

GKE cons

The biggest drawback of GKE clusters is the lack of customizable server configurations. The control plane is fully managed, with no option to deploy in multiple regions for redundancy and high availability. This also makes it difficult to track and manage node activity as the cluster grows, making troubleshooting difficult and time-consuming.

EKS vs. AKS vs. GKE: Comparing cloud-managed Kubernetes offerings

The table below compares the three major public cloud Kubernetes offerings.

Ease of installation and use
  • Automated cluster creation and deployment
  • Config management using several IaC tools (Terraform, CloudFormation, Ansible)
  • Automatically creates the control plane and worker nodes in a single command
  • Offers WebUI, Azure CLI tool, and aksctl tool to manage cluster
  • AKS performs self-healing, upgrades, and patching.
  • Click-based workflow for the creation and management of clusters
  • Prepackaged Kubernetes applications for seamless coordination of auxiliary functionality
Security implementation
  • AWS IAM for Kubernetes RBAC role binding
  • AWS App Mesh for authentication, authorization, and traffic control
  • Azure pod security policies to manage access for services
  • Uses Azure AD for RBAC role binding
  • Implements RBAC role binding using Google’s IAM solution
  • GKE Sandbox isolates untrusted workloads to reduce blast radius.

Starts at $0.10 per cluster per hour. Standard costs for AWS resources are charged additionally.

The AKS control plane is free. Firms must pay only for underlying Azure resources used (e.g., VMs, Blob Storage, VPNs).

  • A standard mode cluster with less than three nodes is free.
  • Pricing begins at $0.10 per cluster per hour for larger standard clusters.
  • Autopilot starts at $0.10 per hour per cluster, and the price varies depending on the number of control planes and nodes deployed.
Cloud service integration

AWS Controllers for Kubernetes (ACK) integrates EKS to other Amazon cloud services

Relies on the Azure service principal to connect and interact with other Azure services

Uses a Config Connector add-on that registers custom objects to GCP resources using CRDs

Deployment options

Mainly deployed on AWS but can also include node pools running on-prem and in other clouds

AKS control plane runs on Azure, with Azure VMs being primary nodes. It can also consume resources deployed on-prem and in other clouds

GKE cluster master runs on GKE, with GCP Compute Engines as the primary worker nodes. GKE supports cluster nodes and resources on hybrid-cloud and on-prem deployments

CI/CD integration

Allows for CI/CD integration with AWS CodePipeline

Azure DevOps pipelines for CI/CD integration

Google Cloud Build for CI/CD integration

On-prem integration

AWS outposts and EKS Anywhere for on-prem deployment

Azure Arc for connection with on-prem clusters

Google Anthos connects GKE to on-prem resources


EKS, AKS, and GKE are three major cloud services used to deploy and manage containers in a Kubernetes-based environment. While all three are known for their resourceful services that come bundled to suit a wide range of cloud-native use cases, there are few differences in how they operate and fare.

EKS provides unlimited compute with the serverless offering, making it ideal for dynamic, service-based applications. Meanwhile, AKS enforces out-of-the-box CI/CD solutions, making it suitable for DevSecOps orchestration and edge computing applications. GKE, on the other hand, supports four-way autoscaling, making it an ideal choice for data-heavy use cases like multimedia streaming and graphic applications.

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