All You Need to Know About EKS (AWS Elastic Kubernetes Service)

Kubernetes has become the go-to solution for container orchestration, with 96% of organizations using or evaluating it (CNCF, 2021). 

AWS introduced Amazon Elastic Kubernetes Service (Amazon EKS), a fully managed solution that companies like Snap and Verizon rely on to simplify deployment. 

With Kubernetes adoption soaring, Amazon EKS helps businesses streamline operations, reduce costs, and enhance scalability. 

In this blog, we’ll explain everything you need to know about Amazon EKS and how it can optimize your cloud-native workloads.

What is Amazon Elastic Kubernetes Service (EKS)?

Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed AWS service that simplifies containerized applications’ deployment, scaling, and operation. 

It automates Kubernetes management tasks, ensuring high availability and security without requiring users to manage the underlying infrastructure.

Core Capabilities of Amazon EKS

Amazon EKS provides a scalable and resilient platform for running Kubernetes clusters on AWS. Its core capabilities include:

• Managed Kubernetes Control Plane – Handles upgrades, patching, and scalability.

• Seamless Integration with AWS Services – Works with AWS IAM, VPC, ALB, CloudWatch, and more.

• Multi-Cluster & Hybrid Deployments – Supports running Kubernetes workloads across AWS, on-premises, and edge environments via Amazon EKS Anywhere and EKS on AWS Outposts.

• Built-in Security & Compliance – Integrates with AWS security tools like IAM, KMS, and Secrets Manager.

How Amazon EKS Works (Control Plane & Worker Nodes)

Amazon EKS follows the standard Kubernetes architecture with:

• Control Plane – Managed by AWS, responsible for scheduling workloads, maintaining cluster state, and handling API requests. It runs across multiple availability zones to ensure high availability.

• Worker Nodes – EC2 instances or AWS Fargate containers that run application workloads. These nodes communicate with the control plane to execute Kubernetes commands.

Key Benefits of Amazon EKS for Enterprises

• Reduced Operational Overhead – AWS manages cluster provisioning, security, and maintenance.

• High Availability & Scalability – Automatic scaling across multiple AZs for resilience.

• Security & Compliance – Deep AWS security integration for role-based access and encryption.

• Hybrid & Multi-Cloud Flexibility – Deploy Kubernetes workloads on AWS, on-premises, or edge locations.

• Optimized Performance & Cost – Supports EC2 Spot Instances, auto-scaling, and AWS Graviton processors for cost efficiency.

Amazon EKS enables enterprises to leverage Kubernetes without the complexity of self-managing clusters, making it a powerful solution for cloud-native applications.

Amazon EKS Architecture

Amazon EKS follows a distributed architecture that ensures high availability, scalability, and security. It comprises a fully managed control plane and worker nodes running applications. 

Additionally, it integrates seamlessly with AWS networking and load-balancing services to optimize performance.

EKS Control Plane: Managed Kubernetes API & etcd

The control plane is the backbone of an Amazon EKS cluster and is fully managed by AWS. It includes:

• Kubernetes API Server – Handles cluster requests and workload scheduling.

• etcd (Key-Value Store) – Stores cluster state and configuration data.

• Automated Scaling & High Availability – Runs across multiple AWS Availability Zones for fault tolerance.

• Built-in Security – Integrated with AWS IAM for authentication and role-based access control (RBAC).

Worker Nodes: EC2, Fargate, and Spot Instances

Worker nodes are where containerized applications run. EKS supports multiple computing options:

• Amazon EC2 Instances – Provides complete control over worker nodes, ideal for custom configurations.

• AWS Fargate – A serverless option that automatically scales workloads without managing infrastructure.

• Spot Instances – Cost-effective compute instances for running fault-tolerant applications at a lower price.

Networking & Load Balancing with VPC, ALB, and NLB

EKS integrates with AWS networking services to ensure efficient communication and traffic management:

• Amazon VPC (Virtual Private Cloud) – Provides network isolation and security for Kubernetes workloads.

• Application Load Balancer (ALB) – Distributes HTTP/HTTPS traffic across services, ideal for microservices.

• Network Load Balancer (NLB) – Handles high-throughput, low-latency traffic with IP-based routing.

With this architecture, Amazon EKS ensures a secure, scalable, and highly available Kubernetes environment, making it easier for enterprises to deploy and manage containerized workloads efficiently.

Core Features of Amazon EKS

Amazon EKS provides robust features that make Kubernetes deployment seamless, scalable, and secure. 

From a fully managed control plane to advanced monitoring and security integrations, EKS is preferred for containerized workloads.

1. Fully Managed Kubernetes Control Plane

Amazon EKS eliminates the need to manage Kubernetes control plane components like API servers and etcd. 

AWS handles patching, updates, and scaling, ensuring high availability and security while reducing operational overhead.

2. High Availability & Auto Scaling

• EKS automatically scales the control plane across multiple Availability Zones (AZs) for fault tolerance.

• Supports Cluster Autoscaler and Horizontal Pod Autoscaler to adjust capacity based on workload demands.

• AWS Auto Scaling Groups help optimize EC2-based worker nodes for performance and cost.

3. Kubernetes Compatibility & Open-Source Tooling

• Fully compliant with upstream Kubernetes, allowing easy migration of workloads.

• Supports popular CNCF tools like Helm, Istio, and Prometheus.

• Works with eksctl, Terraform, and Kubernetes-native CLI tools for cluster management.

4. Security & IAM Authentication for Workload Access

• Integrates with AWS Identity and Access Management (IAM) for fine-grained role-based access control (RBAC).

• Supports AWS PrivateLink for secure API communication.

Provides built-in security policies, encryption, and network segmentation using AWS VPC and Security Groups.

5. Monitoring & Logging with AWS CloudWatch, Prometheus

• Amazon CloudWatch collects and visualizes cluster metrics and logs.

• AWS CloudTrail tracks API activity for compliance and auditing.

• Prometheus & Grafana can be integrated to monitor Kubernetes workloads in-depth.

6. Compute Options: EC2, AWS Fargate, Spot Instances

• Amazon EC2: Full control over worker nodes for customized configurations.

• AWS Fargate: Serverless compute option that eliminates node management.

• Spot Instances: Cost-effective computing for running non-critical workloads at up to 90% lower costs.

With these powerful features, Amazon EKS simplifies Kubernetes operations, enabling businesses to focus on innovation rather than infrastructure management.

Deployment Options for Amazon EKS

Amazon EKS offers flexible deployment models, allowing businesses to run Kubernetes workloads in a way that best suits their operational needs. 

Whether you need a single-region setup, global multi-region redundancy, or hybrid deployments, EKS provides a scalable and secure platform to meet these demands.

Single-Region vs. Multi-Region Deployments

Running an EKS cluster within a single AWS region is sufficient for most organizations. This setup leverages multiple Availability Zones (AZs) to ensure high availability and fault tolerance. 

However, businesses with global applications or strict disaster recovery requirements may opt for a multi-region deployment to improve resilience and reduce latency.

Multi-region deployments leverage:

• AWS Route 53 for intelligent traffic routing across clusters.

• Amazon Global Accelerator to optimize performance by directing users to the nearest healthy endpoint.

• Cross-region replication for critical workloads to ensure failover readiness.

• Running Hybrid Deployments with AWS Outposts

Not all workloads can be fully migrated to the cloud, especially in industries with data sovereignty regulations or low-latency needs. 

Amazon EKS on AWS Outposts bridges this gap by extending EKS to on-premises environments.

With EKS on Outposts, businesses can:

• Deploy Kubernetes clusters closer to their data sources, reducing cloud dependency.

• Maintain a consistent operational model across cloud and on-prem.

• Benefit from AWS security, monitoring, and management while keeping workloads in local data centers.

Edge Deployments with AWS Wavelength & Local Zones

For ultra-low-latency applications, such as real-time gaming, video streaming, and 5G-enabled IoT, Amazon EKS supports edge computing through AWS Wavelength and Local Zones.

• AWS Wavelength integrates EKS with 5G networks, ensuring lightning-fast processing for mobile applications.

• AWS Local Zones bring AWS infrastructure closer to end-users in metro areas, reducing latency for industries like media, healthcare, and financial services.

Use Cases of Amazon EKS

Amazon EKS is a versatile platform that can accommodate various business needs, from running microservices to deploying AI models to managing enterprise applications. 

Here’s how organizations leverage EKS to drive innovation and efficiency.

1. Microservices & Containerized Workloads

EKS is ideal for organizations adopting microservices architectures, where applications are broken down into more minor, independently deployable services. Kubernetes orchestrates these services efficiently, enabling:

• Scalability: Services scale up or down automatically based on demand.

• High availability: Ensures fault tolerance by distributing workloads across multiple Availability Zones.

• DevOps agility: CI/CD pipelines streamline deployment, reducing time-to-market for new features.

Many cloud-native applications rely on EKS to manage their containerized workloads seamlessly while maintaining cost efficiency and operational flexibility.

2. Machine Learning & AI Model Deployment

Deploying and scaling machine learning models requires significant computing resources and orchestration. EKS supports AI/ML workloads by:

• Running model training jobs in parallel using GPU-accelerated EC2 instances.

• Deploying inference models in autoscaling Kubernetes pods to handle real-time predictions.

• Integrating with Kubeflow, TensorFlow, and AWS SageMaker for a seamless ML pipeline.

3. Enterprise & SaaS Applications on EKS

EKS provides a secure and scalable environment for hosting enterprise applications and SaaS platforms, enabling:

• Multi-tenancy: Isolate workloads for different customers using namespaces and RBAC.

• Zero downtime updates: Rolling updates prevent service disruptions.

• Security & compliance: IAM, encryption, and network policies safeguard sensitive data.

Major SaaS providers use EKS to power their services, ensuring reliability, security, and cost efficiency at scale.

4. Hybrid Cloud & On-Prem Kubernetes Deployments

Many enterprises operate in a hybrid cloud environment, running workloads across both on-premises and AWS. EKS supports hybrid deployments through:

• AWS Outposts for running Kubernetes clusters in private data centers.

• EKS Anywhere, which allows organizations to manage Kubernetes clusters across hybrid and multi-cloud setups.

• Consistent Kubernetes experience across cloud and on-prem, simplifying operations and compliance.

Setting Up Amazon EKS

Deploying Kubernetes workloads on Amazon EKS requires proper setup and integration with AWS services. 

This section walks you through the prerequisites, cluster creation, application deployment, and service integration, ensuring a smooth and scalable EKS deployment.

Prerequisites for Amazon EKS Deployment

Before setting up an Amazon EKS cluster, ensure you meet the following requirements:

1. AWS Account & IAM Permissions

• You need an AWS account with the appropriate IAM permissions to create and manage EKS resources.

• The IAM user or role should have policies attached for EKS, EC2, VPC, and IAM operations.

2. AWS CLI & kubectl

• Install and configure the AWS CLI to interact with AWS services from the command line.

• Install kubectl, the Kubernetes command-line tool, for managing clusters and deployments.

3. eksctl (Optional but Recommended)

• The eksctl command-line tool simplifies EKS cluster creation and management.

• It abstracts many manual steps, allowing quick deployments with a single command.

4. VPC & Networking Setup

• EKS requires an adequately configured Amazon VPC with at least two public and private subnets across different Availability Zones.

• Security groups, route tables, and IAM roles must be appropriately configured to allow Kubernetes workloads to function seamlessly.

Creating an Amazon EKS Cluster (AWS Console & CLI)

Once the prerequisites are in place, you can create an EKS cluster using either the AWS Management Console or the CLI (eksctl & AWS CLI).

Using the AWS Console

1. Navigate to the Amazon EKS Dashboard and click on “Create Cluster.”
2. Choose a cluster name and select the Kubernetes version.
3. Configure the VPC and subnets for networking.
4. Define the IAM role for the EKS control plane.
5. Select the compute options (EC2 instances, AWS Fargate, or Spot Instances).
6. Review the configurations and click “Create.”
7. Once the control plane is active, create and attach worker nodes to the cluster.

Using eksctl (Recommended for Quick Deployment)

Alternatively, you can use eksctl to create a cluster with a single command:

eksctl create cluster --name my-cluster --region us-east-1 --nodegroup-name standard-workers --node-type t3.medium --nodes 3

This command:

• Creates a highly available EKS cluster.
• Provisions a managed worker node group with three t3.medium EC2 instances.
• Configures the necessary IAM roles and networking settings.

After cluster creation, verify its status using:

aws eks --region us-east-1 describe-cluster --name my-cluster --query cluster.status

Deploying Kubernetes Applications on Amazon EKS

Once your cluster is up, you can deploy applications using Kubernetes manifests.

Step 1: Configure kubectl to Connect to EKS

aws eks --region us-east-1 update-kubeconfig --name my-cluster

This ensures kubectl can communicate with the EKS control plane.

Step 2: Deploy a Sample Application

Let’s deploy a simple NGINX web server:Create a Kubernetes deployment file (nginx-deployment.yaml):

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:latest
        ports:
        - containerPort: 80

Apply the deployment to the cluster:

kubectl apply -f nginx-deployment.yaml

Expose the application via a Kubernetes Service:

kubectl expose deployment nginx-deployment --type=LoadBalancer --port=80 --target-port=80

Get the external URL to access the service:

kubectl get services

Once deployed, your application will be running on Amazon EKS, with built-in scalability and high availability.

Integrating EKS with AWS Services (S3, RDS, Lambda)

To enhance EKS workloads, businesses often integrate their Kubernetes applications with other AWS services for storage, databases, and serverless computing.

1. Amazon S3 for Persistent Storage

• Applications running in EKS can store files, logs, and backups in Amazon S3.
• Use the AWS IAM OIDC Provider to grant Kubernetes pods access to S3 buckets securely.
• Example: Attach an IAM policy that allows an application to read/write to S3.

2. Amazon RDS for Database Management

• Instead of running a database inside Kubernetes, EKS applications can connect to a fully managed Amazon RDS instance.
• Configure a Kubernetes Secret to store database credentials securely.
• Example:

apiVersion: v1
kind: Secret
metadata:
  name: db-secret
type: Opaque
data:
  username: <base64-encoded-username>
  password: <base64-encoded-password>

3. AWS Lambda for Serverless Workloads

• EKS applications can trigger AWS Lambda functions for event-driven processing.
• Example: An EKS microservice can send logs to Amazon S3, which triggers a Lambda function to process them.

Amazon EKS Best Practices

Optimizing Amazon EKS requires a strategic approach to security, performance, and cost efficiency. By following best practices, organizations can enhance cluster security, maximize performance, and optimize costs, ensuring a robust and cost-effective Kubernetes deployment.

Security Best Practices (RBAC, IAM Policies, Encryption)

Security is a top priority when running Kubernetes clusters on Amazon EKS. Implementing Role-Based Access Control (RBAC), IAM policies, and encryption helps protect workloads from unauthorized access and vulnerabilities.

Implement RBAC for Kubernetes Access

• Use Kubernetes RBAC to restrict permissions based on user roles.
• Define least-privilege access by binding users and services to specific namespaces.
• Example: Creating an RBAC policy to grant read-only access to a developer team:

apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  namespace: dev
  name: read-only-role
rules:
- apiGroups: [""]
  resources: ["pods", "services"]
  verbs: ["get", "list"]

Use AWS IAM for Fine-Grained Access Control

• Enable IAM roles for service accounts (IRSA) to grant granular access to AWS services.
• Assign IAM policies to Kubernetes pods instead of granting permissions to the entire cluster.
• Example: A pod accessing an S3 bucket should have an IAM policy allowing only specific operations.

Enable Encryption for Data Security

• Use AWS Key Management Service (KMS) to encrypt EKS secrets and persistent volumes.
• Enable etcd encryption for securing cluster metadata stored in the control plane.
• Apply TLS encryption for Kubernetes API communication.

Organizations can minimize security risks and safeguard Kubernetes workloads by enforcing strict RBAC, IAM, and encryption policies.

Performance Optimization for Amazon EKS Clusters

To ensure scalability and high availability, Amazon EKS clusters should be fine-tuned for performance and efficiency.

Right-Size Worker Nodes

• Select appropriate EC2 instance types based on workload requirements.
• For specialized workloads, use compute-optimized (C5), memory-optimized (R5), or GPU instances (P4).
• Leverage AWS Fargate for serverless Kubernetes to eliminate node management.

Use Cluster Autoscaler for Dynamic Scaling

• Cluster Autoscaler automatically scales worker nodes based on demand.
• Configure Horizontal Pod Autoscaler (HPA) to adjust replicas dynamically.
• Example: Enabling HPA for an application:

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
  name: nginx-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: nginx
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      targetAverageUtilization: 70

Optimize Networking with VPC CNI & Load Balancers

• Enable Amazon VPC CNI for high-performance networking.
• Use Application Load Balancer (ALB) or Network Load Balancer (NLB) for efficient traffic distribution.
• Implement Service Mesh (AWS App Mesh, Istio) for advanced traffic control.

EKS clusters can achieve optimal performance with minimal overhead by fine-tuning compute resources, autoscaling, and networking.

Cost Optimization & Efficient Resource Allocation

Running Amazon EKS efficiently means optimizing resources to reduce unnecessary costs while maintaining performance.

Leverage AWS Spot Instances

• Use Spot Instances for non-critical workloads to save up to 90% on compute costs.
• Configure Spot Instance interruptions with On-Demand fallback for better resilience.
• Example: Defining a mixed-instance policy:

nodeGroups:
  - name: spot-workers
    instanceTypes: ["c5.large", "m5.large"]
    spot: true
    minSize: 1
    maxSize: 5

Use Fargate for Cost-Effective Serverless Deployments

• AWS Fargate removes the need to provision and manage EC2 instances.
• Ideal for small, unpredictable workloads where per-second billing reduces waste.

Enable Kubernetes Resource Requests & Limits

• Prevent over-provisioning by defining CPU & memory limits for containers.
• Example: Setting resource requests and limits:

resources:
  requests:
    cpu: "500m"
    memory: "256Mi"
  limits:
    cpu: "1"
    memory: "512Mi"

Amazon EKS vs. Other Kubernetes Solutions

Choosing the right Kubernetes deployment model depends on factors like management overhead, cost, scalability, and integration with cloud services. 

Let’s compare Amazon EKS with self-managed Kubernetes, AWS Fargate, and other cloud-based Kubernetes services to see how they stack up.

Amazon EKS vs. Self-Managed Kubernetes on AWS

Feature	Amazon EKS	Self-Managed Kubernetes on AWS
Management	Fully managed control plane	Requires manual setup and maintenance
Security & Updates	AWS handles security patches & upgrades	Admin must apply patches manually
Availability	Multi-AZ, highly available	Single-point failures possible
Scaling	Auto-scaling built-in	Manual cluster scaling is needed
Cost	EKS charges $0.10 per hour for control plane	No control-plane fees but high operational costs

• When to choose EKS: Ideal for teams that want a fully managed service with less operational overhead.

• When to choose Self-Managed Kubernetes: Suitable for organizations needing full customization and control over their Kubernetes clusters.

Amazon EKS vs. AWS Fargate (Serverless Kubernetes)

Feature	Amazon EKS	AWS Fargate
Node Management	The user manages EC2 worker nodes	Fully managed, with no infrastructure to maintain
Use Case	Best for long-running workloads	Best for event-driven & short-lived workloads
Scaling	Cluster Autoscaler, HPA	Auto-scales based on demand
Cost Model	Pay for EC2 instances	Pay per pod with no idle costs

• When to choose EKS: Best for enterprises running large, persistent Kubernetes workloads that require control over nodes.

• When to choose AWS Fargate: Great for serverless Kubernetes, where teams don’t want to manage infrastructure.

Amazon EKS vs. GKE (Google Kubernetes Engine) & AKS (Azure Kubernetes Service)

Feature	Amazon EKS	GKE (Google Kubernetes Engine)	AKS (Azure Kubernetes Service)
Cloud Provider	AWS	Google Cloud	Microsoft Azure
Ease of Setup	Medium	Easiest	Easy
Control Plane Cost	$0.10 per hour	Free (for autopilot mode)	Free
Autoscaling	Node & pod autoscaling	Advanced autoscaling features	Standard autoscaling
Hybrid & On-Prem Support	AWS Outposts	Anthos	Azure Arc
Best for	AWS-centric workloads	AI/ML & hybrid cloud	Azure ecosystem users

• When to choose Amazon EKS: Best for AWS users who want deep integration with AWS services.

• When to choose GKE: Ideal for teams running AI/ML workloads with Google Cloud services.

• When to choose AKS: Best for Microsoft-centric enterprises using Azure services like Active Directory and SQL Server.

Common Challenges & Troubleshooting Amazon EKS

Even though Amazon EKS simplifies Kubernetes management, users may still encounter operational challenges. 

Here’s a breakdown of some common issues and how to troubleshoot them.

1. Networking & Connectivity Issues

Common Problems:

• Pods can’t communicate across nodes
• Ingress traffic is blocked
• Issues with DNS resolution in the cluster

Troubleshooting Steps:

1. Check VPC & Security Groups: Ensure worker nodes and control plane are in the same VPC and subnets with proper security group rules.
2. Verify Kubernetes Network Policies: Network policies may restrict traffic between pods. Use kubectl describe networkpolicy to inspect them.

3. DNS Resolution Fix: Restart coredns pods:

kubectl rollout restart deployment coredns -n kube-system

4. Ingress/Load Balancer Issues – Ensure your ALB (Application Load Balancer) or NLB (Network Load Balancer) has the correct target groups and security settings.

2. Pod Scheduling & Scaling Limitations

Common Problems:

• Pods stuck in “Pending” state
• Node resources exhausted (CPU, memory)
• Autoscaling not triggering

Troubleshooting Steps:

1. Check Pod Events & Logs – Run:

kubectl describe pod

Look for resource constraints or scheduling issues.

2. Monitor Node Resources – Use:

kubectl top nodes

If nodes are fully utilized, consider adding more capacity.

3. Enable Cluster Autoscaler – Ensure Cluster Autoscaler is configured correctly to scale EC2 instances dynamically.

4. Check Taints & Tolerations – Ensure pods are not tainted from scheduling on available nodes.

3. Security & Compliance Considerations

Common Problems:

• IAM role misconfigurations prevent services from accessing AWS resources
• RBAC permissions block users or services
• Unencrypted secrets stored in Kubernetes manifests

Troubleshooting Steps:

1. Validate IAM Roles & Policies – Use AWS IAM Policy Simulator to test permissions before applying them.

2. Review RBAC Settings – Ensure users and services have the correct roles and role bindings in Kubernetes.

3. Encrypt Secrets – Use AWS Secrets Manager or Kubernetes Secrets with encryption enabled instead of storing plain text credentials.

Final Thoughts 

Amazon EKS simplifies Kubernetes management, offering enterprises a scalable, secure, and cost-efficient way to run containerized applications. 

With its fully managed control plane, deep AWS integrations, and flexibility across EC2, Fargate, and hybrid deployments, EKS is a solid choice for businesses looking to streamline container orchestration. 

However, optimizing performance, security, and cost efficiency requires expert guidance and strategic implementation. This is where CrossAsyst can help.

Why Choose CrossAsyst for Your Amazon EKS Deployment?

At CrossAsyst, we specialize in cloud-native solutions, ensuring seamless EKS deployment, optimization, and security compliance tailored to your business needs. 

Whether you’re looking to migrate to Kubernetes, optimize your existing EKS clusters, or integrate AWS services for enhanced scalability, our team of experts has you covered.

• End-to-End EKS Implementation
• Performance Tuning & Cost Optimization
• Security-First Approach with IAM & Compliance Best Practices
• Seamless AWS Service Integrations (S3, RDS, Lambda, etc.)

Ready to maximize the potential of Amazon EKS? Contact CrossAsyst today and take your Kubernetes infrastructure to the next level!