Amazon EKS Cluster: A Deep Dive in AWS Resources & Best Practices to Adopt
Container orchestration has become the foundation of modern cloud-native applications, with organizations increasingly adopting Kubernetes to manage their containerized workloads at scale. As companies move beyond simple deployments and begin operating hundreds or thousands of containers across diverse environments, the complexity of managing Kubernetes infrastructure grows exponentially. Managing the Kubernetes control plane, ensuring high availability, handling security patches, and scaling worker nodes requires significant expertise and operational overhead that many teams struggle to maintain.
Amazon Elastic Kubernetes Service (EKS) addresses these challenges by providing a fully managed Kubernetes service that eliminates the heavy lifting of cluster management while preserving the flexibility and power of Kubernetes. According to the Cloud Native Computing Foundation's 2023 survey, 71% of organizations are running Kubernetes in production, with managed services like EKS representing the fastest-growing segment of Kubernetes deployments. This growth reflects the industry's recognition that while Kubernetes provides powerful orchestration capabilities, managing the underlying infrastructure should not be a differentiator for most organizations.
The strategic importance of EKS extends beyond simple convenience. In enterprise environments where application availability can mean the difference between meeting SLAs and facing significant financial penalties, the reliability guarantees provided by AWS's managed control plane become critical business enablers. Organizations report that migrating to EKS typically reduces their Kubernetes operational overhead by 60-80%, allowing platform teams to focus on application development rather because infrastructure management. The AWS EKS service handles control plane operations, security patches, and scaling automatically, while providing integration with the broader AWS ecosystem for logging, monitoring, and security services.
In this blog post we will learn about what EKS clusters are, how you can configure and work with them using Terraform, and learn about the best practices for this service.
What is Amazon EKS?
Amazon EKS is a fully managed Kubernetes service that runs the Kubernetes control plane across multiple AWS availability zones to provide high availability and fault tolerance. The service eliminates the need for organizations to install, operate, and maintain their own Kubernetes control plane infrastructure, handling the complexity of master node provisioning, patching, and scaling automatically.
The EKS architecture separates the control plane from the worker nodes, with AWS managing the control plane components including the API server, etcd, and scheduler, while customers retain full control over their worker nodes. This separation allows organizations to benefit from AWS's operational expertise in running Kubernetes at scale while maintaining the flexibility to customize their compute environment according to their specific requirements. The control plane runs on a dedicated AWS infrastructure that's isolated from other customers, providing both security and performance benefits.
Each EKS cluster operates within your AWS account but uses a shared responsibility model where AWS handles the underlying infrastructure and you manage the applications and configurations. The service automatically detects and replaces unhealthy control plane instances, performs automatic version upgrades when configured, and provides integrated logging and monitoring through CloudWatch. This managed approach reduces the operational burden significantly compared to self-managed Kubernetes deployments, where teams must handle everything from OS patching to disaster recovery planning.
Control Plane Management and Architecture
The EKS control plane architecture represents one of the most sophisticated aspects of the service, running across multiple availability zones to provide 99.95% SLA availability. Unlike self-managed Kubernetes clusters where the control plane becomes a single point of failure, EKS distributes the control plane components across at least two availability zones, with automatic failover capabilities built into the service design.
The control plane includes all the standard Kubernetes components: the API server that handles all REST commands, the etcd database that stores cluster state, the scheduler that assigns pods to nodes, and the controller manager that runs core control loops. AWS manages the operating system, security patches, and hardware replacement for all these components without any customer intervention required. The control plane also includes the AWS-specific components like the VPC CNI plugin for networking and the AWS Load Balancer Controller for service integration.
What makes EKS particularly powerful is how AWS has integrated the control plane with native AWS services. The API server seamlessly integrates with AWS Identity and Access Management (IAM) for authentication and authorization, allowing organizations to use their existing AWS security model rather than managing separate Kubernetes RBAC systems. The control plane also integrates with AWS CloudTrail for audit logging, VPC Flow Logs for network monitoring, and AWS Config for compliance checking, providing a comprehensive observability stack that would be complex to implement with self-managed Kubernetes.
The networking architecture deserves special attention as it affects how applications communicate both within the cluster and with external services. EKS uses the Amazon VPC CNI plugin by default, which assigns actual VPC IP addresses to pods rather than using overlay networking. This approach provides better network performance and simplifies security group rules, but it also means that subnet IP address planning becomes more critical as each pod consumes a VPC IP address. The control plane automatically manages the ENI (Elastic Network Interface) allocation and IP assignment, but understanding these networking fundamentals is crucial for designing scalable EKS deployments.
Worker Node Integration and Compute Options
While AWS manages the control plane, EKS provides multiple options for worker node management, each with different trade-offs between convenience and control. The most common approach uses managed node groups, where AWS handles the provisioning, scaling, and lifecycle management of EC2 instances that join your cluster. Managed node groups automatically handle tasks like node registration, security group configuration, and rolling updates, making them ideal for most production workloads.
The EKS node group service integrates deeply with Auto Scaling Groups to provide automatic scaling based on cluster demand. When pods cannot be scheduled due to insufficient resources, the cluster autoscaler can automatically add new nodes to handle the load. Similarly, when nodes become underutilized, the system can safely drain and terminate instances to optimize costs. This elastic scaling capability makes EKS particularly suitable for workloads with variable demand patterns.
For organizations requiring more control over the underlying infrastructure, EKS supports self-managed node groups where you maintain full control over EC2 instances while still benefiting from the managed control plane. This approach requires more operational overhead but provides flexibility for specialized requirements like custom AMIs, specific instance types, or complex networking configurations. Self-managed nodes must still register with the EKS cluster and maintain compatibility with the control plane, but you handle all aspects of instance lifecycle management.
AWS Fargate represents the most abstracted compute option, allowing you to run pods without managing any EC2 instances at all. With Fargate, you simply specify the CPU and memory requirements for your pods, and AWS handles all the underlying infrastructure provisioning and management. This serverless approach to Kubernetes compute eliminates node management entirely but comes with some limitations around networking, storage, and pod specifications. Fargate is particularly well-suited for batch workloads, microservices, and applications that can operate within its constraints.
Strategic Business Impact of EKS
The strategic value of EKS extends far beyond technical convenience, fundamentally changing how organizations approach application deployment and infrastructure management. Companies adopting EKS report significant improvements in developer productivity, operational efficiency, and time-to-market for new applications. The managed nature of the service allows platform teams to focus on building developer experiences rather than maintaining infrastructure, while the Kubernetes compatibility ensures applications remain portable across different environments.
From a financial perspective, EKS provides predictable operational costs compared to self-managed Kubernetes deployments. Organizations typically see 40-60% reduction in infrastructure management costs when migrating from self-managed clusters to EKS, primarily through reduced operational overhead and more efficient resource utilization. The service's integration with AWS cost management tools also provides better visibility into application-level costs, enabling more accurate chargeback and cost optimization strategies.
Operational Excellence and Reliability
EKS transforms operational excellence by providing enterprise-grade reliability guarantees that would be expensive and complex to achieve with self-managed Kubernetes. The 99.95% SLA for the control plane, backed by AWS's operational expertise and infrastructure redundancy, means that cluster downtime becomes a rare occurrence rather than a regular operational concern. This reliability enables organizations to confidently run mission-critical workloads on EKS without the extensive disaster recovery planning required for self-managed clusters.
The service's integration with AWS operational tools creates a unified management experience across the entire infrastructure stack. CloudWatch integration provides comprehensive monitoring and alerting capabilities, while AWS Config ensures compliance with organizational policies and industry regulations. The automatic patching and upgrade capabilities mean that security vulnerabilities are addressed promptly without manual intervention, reducing the attack surface and improving overall security posture.
The operational benefits extend to the development workflow as well. EKS clusters can be easily integrated with CI/CD pipelines, enabling automated testing and deployment processes that improve code quality and reduce manual errors. The service's compatibility with popular Kubernetes tools like Helm, Istio, and Prometheus means that existing workflows and toolchains can be preserved while gaining the benefits of managed infrastructure. This compatibility reduces the learning curve for teams migrating from self-managed Kubernetes environments.
Developer Productivity and Innovation
EKS accelerates developer productivity by abstracting away infrastructure complexity and providing consistent, reliable environments for application development and testing. Developers can focus on writing code rather than debugging infrastructure issues, leading to faster feature development and improved application quality. The service's integration with AWS developer tools like CodeCommit, CodeBuild, and CodeDeploy creates seamless workflows from code commit to production deployment.
The managed nature of EKS enables organizations to implement more sophisticated deployment patterns like blue-green deployments, canary releases, and feature flags without requiring deep Kubernetes expertise from every development team. Platform teams can create standardized deployment templates and policies that development teams can consume through self-service interfaces, improving consistency while maintaining developer autonomy.
Enterprise Integration and Compliance
EKS provides enterprise-grade integration capabilities that are particularly valuable for organizations with complex compliance requirements or existing enterprise toolchains. The service's integration with AWS IAM enables centralized identity management and fine-grained access control, while integration with AWS Secrets Manager provides secure secret management without requiring additional tools or processes.
For organizations operating in regulated industries, EKS provides the compliance features necessary to meet strict regulatory requirements. The service supports encryption at rest and in transit, comprehensive audit logging, and network isolation capabilities that satisfy requirements for PCI DSS, HIPAA, and SOC compliance. The integration with AWS compliance tools like AWS Config and AWS Security Hub provides continuous compliance monitoring and automated remediation capabilities.
Key Features and Capabilities
Integrated Security and Identity Management
EKS provides comprehensive security features that integrate seamlessly with AWS's identity and access management ecosystem. The service supports AWS IAM for cluster authentication, allowing organizations to use their existing AWS user and role management processes for Kubernetes access control. This integration eliminates the need to manage separate authentication systems and provides consistent security policies across the entire AWS infrastructure.
The security model extends to pod-level controls through IAM roles for service accounts (IRSA), which allows individual pods to assume specific AWS IAM roles without requiring node-level permissions. This fine-grained access control enables the principle of least privilege at the application level, significantly improving security posture compared to traditional approaches where all pods on a node share the same permissions.
Native AWS Service Integration
One of EKS's most powerful capabilities is its deep integration with the broader AWS ecosystem. The service automatically configures network integration with Amazon VPC, enabling pods to communicate with other AWS services using native networking rather than complex proxy configurations. This integration extends to load balancing through the AWS Load Balancer Controller, which automatically provisions and manages Application Load Balancers and Network Load Balancers for Kubernetes services.
Storage integration with Amazon EBS, EFS, and FSx provides persistent storage options that are automatically provisioned and managed through Kubernetes storage classes. The EFS integration is particularly valuable for applications requiring shared storage across multiple pods, while EBS integration provides high-performance block storage for stateful applications.
Automatic Scaling and Resource Management
EKS includes sophisticated scaling capabilities that automatically adjust cluster capacity based on application demand. The cluster autoscaler monitors pod scheduling and automatically adds or removes nodes based on resource requirements, while the horizontal pod autoscaler scales individual applications based on CPU utilization or custom metrics. This multi-level scaling ensures optimal resource utilization while maintaining application performance.
The service's integration with AWS Auto Scaling Groups provides additional scaling capabilities, including scheduled scaling for predictable workload patterns and integration with AWS Application Auto Scaling for more complex scaling scenarios. The Auto Scaling Group integration enables sophisticated scaling policies that consider both cluster-level and application-level metrics.
Comprehensive Monitoring and Observability
EKS provides extensive monitoring and observability capabilities through integration with AWS CloudWatch and support for popular open-source monitoring tools. The service automatically collects cluster metrics, API server logs, and control plane events, providing comprehensive visibility into cluster health and performance. This monitoring capability extends to application-level metrics through integration with the CloudWatch Container Insights service.
The observability stack includes distributed tracing capabilities through AWS X-Ray integration, enabling detailed analysis of application performance and dependencies. The CloudWatch alarm integration provides automated alerting and response capabilities, allowing teams to detect and respond to issues before they impact users.
Integration Ecosystem
EKS operates within a rich ecosystem of AWS services and third-party tools that enhance its capabilities and provide comprehensive solutions for modern application deployment and management. The service's open architecture and standard Kubernetes APIs ensure compatibility with the vast majority of cloud-native tools and frameworks, while AWS's managed integrations provide seamless connectivity with native AWS services.
At the time of writing there are 50+ AWS services that integrate with EKS in some capacity. These integrations range from core infrastructure services like VPC and IAM to specialized services like AWS Batch for job processing and Amazon SageMaker for machine learning workloads. The breadth of these integrations makes EKS a natural choice for organizations already invested in the AWS ecosystem.
The compute integration includes multiple options for worker nodes, from traditional EC2 instances managed through Auto Scaling Groups to serverless computing through AWS Fargate. The EC2 integration provides maximum flexibility and control, while Fargate eliminates infrastructure management entirely. This variety of compute options allows organizations to optimize for their specific requirements around cost, performance, and operational complexity.
Storage integrations provide persistent storage options that are automatically provisioned and managed through Kubernetes storage classes. The EBS integration provides high-performance block storage for stateful applications, while EFS integration enables shared storage across multiple pods and availability zones. These storage integrations handle the complexity of volume provisioning, encryption, and lifecycle management automatically.
Networking integration with Amazon VPC provides native connectivity between pods and other AWS services without requiring complex networking configurations. The VPC integration includes automatic subnet selection, security group management, and DNS resolution, while the AWS Load Balancer Controller automatically provisions and manages Application Load Balancers for Kubernetes services.
Pricing and Scale Considerations
EKS pricing follows a straightforward model with a flat rate of $0.10 per hour for each cluster control plane, regardless of the size or complexity of the cluster. This pricing model makes EKS cost-effective for both small development clusters and large production deployments, with the control plane costs representing a small fraction of the total infrastructure costs for most workloads.
The worker node costs depend on the compute option chosen. For managed node groups and self-managed nodes, you pay standard EC2 pricing for the instances, while Fargate has its own pricing model based on the vCPU and memory resources allocated to pods. The choice between these options often depends on workload characteristics, operational requirements, and cost optimization goals.
Scale Characteristics
EKS supports massive scale with individual clusters capable of hosting thousands of nodes and hundreds of thousands of pods. The service's architecture handles the complexity of managing large clusters automatically, including distributing API server load, managing etcd performance, and scaling network components. The managed control plane automatically scales to handle increased API requests and cluster state management without manual intervention.
The scaling characteristics vary by compute option. Managed node groups can scale to hundreds of nodes per group, with multiple node groups per cluster supporting different instance types and configurations. Fargate provides effectively unlimited scaling for individual pods, with AWS handling all the underlying infrastructure provisioning and management.
Enterprise Considerations
For enterprise deployments, EKS provides additional features that support complex organizational requirements. Multi-cluster management capabilities enable organizations to run multiple clusters across different environments, regions, or business units while maintaining centralized governance and security policies. The service's integration with AWS Organizations enables centralized billing and account management across multiple clusters.
The enterprise security features include comprehensive audit logging, encryption at rest and in transit, and integration with enterprise identity providers through AWS IAM Identity Center. These features enable EKS to meet the strict security and compliance requirements typical of enterprise environments.
EKS competes with other managed Kubernetes services like Google Kubernetes Engine (GKE) and Azure Kubernetes Service (AKS), as well as self-managed Kubernetes distributions. However, for infrastructure running on AWS this is the natural choice given the deep integration with AWS services and the operational benefits of using a managed service within the same cloud provider ecosystem.
The total cost of ownership for EKS typically proves lower than self-managed alternatives when accounting for the operational overhead, reliability improvements, and reduced time-to-market for new applications. Organizations report that the managed service eliminates the need for specialized Kubernetes platform teams, allowing existing development and operations teams to manage EKS clusters effectively.
Managing Amazon EKS Clusters using Terraform
Setting up an EKS cluster through Terraform presents moderate complexity, as it requires careful orchestration of multiple AWS resources including VPC networking, IAM roles, security groups, and node groups. While the core aws_eks_cluster resource itself is straightforward, production-ready EKS clusters require proper configuration of networking, security, and worker node infrastructure that can span dozens of related resources.
Production-Ready EKS Cluster with Managed Node Groups
For most organizations, a production EKS cluster needs to handle real workloads with proper networking isolation, security controls, and scalable worker nodes. This configuration establishes a complete EKS environment suitable for production applications.
# VPC and networking foundation
resource "aws_vpc" "eks_vpc" {
cidr_block = "10.0.0.0/16"
enable_dns_hostnames = true
enable_dns_support = true
tags = {
Name = "eks-production-vpc"
Environment = "production"
Service = "kubernetes"
}
}
resource "aws_subnet" "eks_private_subnets" {
count = 2
vpc_id = aws_vpc.eks_vpc.id
cidr_block = "10.0.${count.index + 1}.0/24"
availability_zone = data.aws_availability_zones.available.names[count.index]
tags = {
Name = "eks-private-subnet-${count.index + 1}"
"kubernetes.io/role/internal-elb" = "1"
"kubernetes.io/cluster/production-cluster" = "owned"
}
}
resource "aws_subnet" "eks_public_subnets" {
count = 2
vpc_id = aws_vpc.eks_vpc.id
cidr_block = "10.0.${count.index + 10}.0/24"
availability_zone = data.aws_availability_zones.available.names[count.index]
map_public_ip_on_launch = true
tags = {
Name = "eks-public-subnet-${count.index + 1}"
"kubernetes.io/role/elb" = "1"
"kubernetes.io/cluster/production-cluster" = "owned"
}
}
# EKS cluster service role
resource "aws_iam_role" "eks_cluster_role" {
name = "eks-cluster-service-role"
assume_role_policy = jsonencode({
Version = "2012-10-17"
Statement = [
{
Action = "sts:AssumeRole"
Effect = "Allow"
Principal = {
Service = "eks.amazonaws.com"
}
}
]
})
}
resource "aws_iam_role_policy_attachment" "eks_cluster_policy" {
policy_arn = "arn:aws:iam::aws:policy/AmazonEKSClusterPolicy"
role = aws_iam_role.eks_cluster_role.name
}
# EKS cluster security group
resource "aws_security_group" "eks_cluster_sg" {
name = "eks-cluster-security-group"
description = "Security group for EKS cluster control plane"
vpc_id = aws_vpc.eks_vpc.id
ingress {
from_port = 443
to_port = 443
protocol = "tcp"
cidr_blocks = ["10.0.0.0/16"]
}
egress {
from_port = 0
to_port = 0
protocol = "-1"
cidr_blocks = ["0.0.0.0/0"]
}
tags = {
Name = "eks-cluster-sg"
}
}
# Main EKS cluster resource
resource "aws_eks_cluster" "production_cluster" {
name = "production-cluster"
role_arn = aws_iam_role.eks_cluster_role.arn
version = "1.28"
vpc_config {
subnet_ids = concat(aws_subnet.eks_private_subnets[*].id, aws_subnet.eks_public_subnets[*].id)
endpoint_private_access = true
endpoint_public_access = true
public_access_cidrs = ["0.0.0.0/0"]
security_group_ids = [aws_security_group.eks_cluster_sg.id]
}
# Enable control plane logging
enabled_cluster_log_types = ["api", "audit", "authenticator", "controllerManager", "scheduler"]
depends_on = [
aws_iam_role_policy_attachment.eks_cluster_policy,
aws_cloudwatch_log_group.eks_cluster_logs
]
tags = {
Environment = "production"
Team = "platform-engineering"
Service = "kubernetes"
}
}
# CloudWatch log group for EKS cluster logs
resource "aws_cloudwatch_log_group" "eks_cluster_logs" {
name = "/aws/eks/production-cluster/cluster"
retention_in_days = 7
tags = {
Environment = "production"
Service = "kubernetes"
}
}
The cluster configuration shown above demonstrates several important patterns for production EKS deployments. The vpc_config block specifies both private and public subnets, allowing the cluster to support both internal services and load balancers that need internet access. The endpoint_private_access and endpoint_public_access settings control how the Kubernetes API server can be reached - enabling both allows maximum flexibility while maintaining security through security groups. The enabled_cluster_log_types parameter activates comprehensive logging for audit and troubleshooting purposes.
The subnet tagging shown here is critical for EKS functionality. The kubernetes.io/role/internal-elb tag on private subnets tells the AWS Load Balancer Controller where to place internal load balancers, while kubernetes.io/role/elb on public subnets designates where internet-facing load balancers should be created. The kubernetes.io/cluster/cluster-name tag with value "owned" indicates that these subnets are dedicated to this EKS cluster, allowing Kubernetes to manage resources like elastic load balancers and security groups automatically.
Advanced EKS Cluster with Custom Node Groups and Add-ons
For organizations requiring fine-grained control over worker node configuration or specialized compute requirements, custom node groups and EKS add-ons provide additional capabilities. This configuration demonstrates advanced patterns for heterogeneous workloads.
# Node group IAM role
resource "aws_iam_role" "eks_node_role" {
name = "eks-node-group-role"
assume_role_policy = jsonencode({
Version = "2012-10-17"
Statement = [
{
Action = "sts:AssumeRole"
Effect = "Allow"
Principal = {
Service = "ec2.amazonaws.com"
}
}
]
})
}
resource "aws_iam_role_policy_attachment" "eks_worker_node_policy" {
policy_arn = "arn:aws:iam::aws:policy/AmazonEKSWorkerNodePolicy"
role = aws_iam_role.eks_node_role.name
}
resource "aws_iam_role_policy_attachment" "eks_cni_policy" {
policy_arn = "arn:aws:iam::aws:policy/AmazonEKS_CNI_Policy"
role = aws_iam_role.eks_node_role.name
}
resource "aws_iam_role_policy_attachment" "eks_container_registry_policy" {
policy_arn = "arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryReadOnly"
role = aws_iam_role.eks_node_role.name
}
# Launch template for customized worker nodes
resource "aws_launch_template" "eks_nodes" {
name_prefix = "eks-node-template-"
image_id = data.aws_ami.eks_worker.id
instance_type = "t3.medium"
key_name = aws_key_pair.eks_nodes.key_name
vpc_security_group_ids = [aws_security_group.eks_node_sg.id]
user_data = base64encode(templatefile("${path.module}/userdata.sh", {
cluster_name = aws_eks_cluster.production_cluster.name
cluster_endpoint = aws_eks_cluster.production_cluster.endpoint
cluster_ca = aws_eks_cluster.production_cluster.certificate_authority[0].data
bootstrap_arguments = "--container-runtime containerd --kubelet-extra-args '--node-labels=nodegroup=general'"
}))
block_device_mappings {
device_name = "/dev/xvda"
ebs {
volume_size = 50
volume_type = "gp3"
encrypted = true
}
}
tag_specifications {
resource_type = "instance"
tags = {
Name = "eks-worker-node"
Environment = "production"
NodeGroup = "general"
}
}
}
# General purpose managed node group
resource "aws_eks_node_group" "general_nodes" {
cluster_name = aws_eks_cluster.production_cluster.name
node_group_name = "general-nodes"
node_role_arn = aws_iam_role.eks_node_role.arn
subnet_ids = aws_subnet.eks_private_subnets[*].id
capacity_type = "ON_DEMAND"
instance_types = ["t3.medium"]
scaling_config {
desired_size = 2
max_size = 4
min_size = 1
}
update_config {
max_unavailable_percentage = 25
}
launch_template {
id = aws_launch_template.eks_nodes.id
version = "$Latest"
}
depends_on = [
aws_iam_role_policy_attachment.eks_worker_node_policy,
aws_iam_role_policy_attachment.eks_cni_policy,
aws_iam_role_policy_attachment.eks_container_registry_policy,
]
tags = {
Environment = "production"
NodeGroup = "general"
}
}
# Spot instance node group for cost optimization
resource "aws_eks_node_group" "spot_nodes" {
cluster_name = aws_eks_cluster.production_cluster.name
node_group_name = "spot-nodes"
node_role_arn = aws_iam_role.eks_node_role.arn
subnet_ids = aws_subnet.eks_private_subnets[*].id
capacity_type = "SPOT"
instance_types = ["t3.medium", "t3.large", "t3a.medium", "t3a.large"]
scaling_config {
desired_size = 1
max_size = 6
min_size = 0
}
update_config {
max_unavailable_percentage = 50
}
# Spot instances can be interrupted, so allow higher unavailability
taint {
key = "spot-instance"
value = "true"
effect = "NO_SCHEDULE"
}
tags = {
Environment = "production"
NodeGroup = "spot"
CostOptimized = "true"
}
}
# EKS add-ons for enhanced functionality
resource "aws_eks_addon" "vpc_cni" {
cluster_name = aws_eks_cluster.production_cluster.name
addon_name = "vpc-cni"
resolve_conflicts = "OVERWRITE"
depends_on = [aws_eks_node_group.general_nodes]
}
resource "aws_eks_addon" "kube_proxy" {
cluster_name = aws_eks_cluster.production_cluster.name
addon_name = "kube-proxy"
resolve_conflicts = "OVERWRITE"
depends_on = [aws_eks_node_group.general_nodes]
}
resource "aws_eks_addon" "coredns" {
cluster_name = aws_eks_cluster.production_cluster.name
addon_name = "coredns"
resolve_conflicts = "OVERWRITE"
depends_on = [aws_eks_node_group.general_nodes]
}
# Security group for worker nodes
resource "aws_security_group" "eks_node_sg" {
name = "eks-node-security-group"
description = "Security group for EKS worker nodes"
vpc_id = aws_vpc.eks_vpc.id
ingress {
from_port = 0
to_port = 65535
protocol = "tcp"
self = true
}
ingress {
from_port = 1025
to_port = 65535
protocol = "tcp"
security_groups = [aws_security_group.eks_cluster_sg.id]
}
egress {
from_port = 0
to_port = 0
protocol = "-1"
cidr_blocks = ["0.0.0.0/0"]
}
tags = {
Name = "eks-node-sg"
"kubernetes.io/cluster/production-cluster" = "owned"
}
}
This advanced configuration showcases several sophisticated EKS patterns. The launch template approach provides granular control over worker node configuration, including custom AMIs, instance storage, and user data scripts. The dual node group setup demonstrates a common cost optimization strategy where stable workloads run on on-demand instances while batch or fault-tolerant workloads can utilize spot instances with appropriate taints and tolerations.
The EKS add-ons shown here represent the modern approach to managing cluster components. Rather than deploying these components manually through Kubernetes manifests, EKS add-ons provide managed versions that AWS maintains and updates automatically. The vpc-cni add-on manages pod networking, kube-proxy handles service networking, and coredns provides DNS resolution within the cluster. These add-ons integrate with AWS VPC endpoints and other AWS services for optimal performance and security.
The node group configuration parameters deserve special attention. The capacity_type setting allows mixing on-demand and spot instances for cost optimization. The scaling_config block defines auto-scaling behavior, while update_config controls how rolling updates are performed. For production clusters, setting max_unavailable_percentage to 25% ensures gradual node replacements that maintain application availability.
Security group configuration for EKS requires careful attention to both cluster-to-node and node-to-node communication. The security groups shown here follow AWS best practices by allowing the control plane to communicate with worker nodes on the necessary ports while permitting inter-node communication for pod networking. The kubernetes.io/cluster/cluster-name tag on security groups enables the cluster to automatically manage additional security group rules for services like Application Load Balancers created by the AWS Load Balancer Controller.
Best practices for Amazon EKS Cluster
Managing EKS clusters effectively requires a comprehensive approach that balances security, performance, cost optimization, and operational efficiency. These practices have been refined through real-world enterprise deployments and reflect the lessons learned from running production workloads at scale.
Configure Resource Quotas and Limits
Why it matters: Without proper resource controls, individual applications can consume excessive cluster resources, leading to resource starvation for other workloads and potential cluster instability. Resource quotas provide namespace-level controls, while limits ensure individual containers don't monopolize resources.
Implementation: Start by implementing ResourceQuota objects for each namespace and set default LimitRange policies. Monitor actual resource usage patterns for 2-4 weeks before setting final limits to avoid artificially constraining legitimate workloads.
# Create namespace-level resource quota
kubectl apply -f - <<EOF
apiVersion: v1
kind: ResourceQuota
metadata:
name: production-quota
namespace: production
spec:
hard:
requests.cpu: "10"
requests.memory: 20Gi
limits.cpu: "20"
limits.memory: 40Gi
pods: "50"
EOF
Monitor quota usage regularly and adjust based on application growth patterns. Set up CloudWatch alerts when quota utilization exceeds 80% to proactively scale resources before hitting limits.
Implement Comprehensive Network Security
Why it matters: EKS clusters operate in a shared network environment where improper network configuration can expose sensitive workloads to unauthorized access. Network policies provide microsegmentation capabilities that prevent lateral movement between compromised services.
Implementation: Deploy a CNI plugin that supports NetworkPolicy (like Calico or Cilium) and implement a default-deny policy for all namespaces, then selectively allow required traffic flows.
# Example NetworkPolicy for database access
resource "kubernetes_network_policy" "database_access" {
metadata {
name = "database-access-policy"
namespace = "production"
}
spec {
pod_selector {
match_labels = {
app = "database"
}
}
policy_types = ["Ingress"]
ingress {
from {
pod_selector {
match_labels = {
tier = "backend"
}
}
}
ports {
port = "5432"
protocol = "TCP"
}
}
}
}
Regularly audit network policies and validate that they align with your application's actual communication patterns. Use tools like kubectl describe networkpolicy to verify policy effectiveness.
Establish Robust Node Group Management
Why it matters: Node groups represent the compute foundation of your EKS cluster, and their configuration directly impacts application performance, availability, and costs. Proper node group management prevents resource bottlenecks and ensures optimal workload distribution.
Implementation: Create multiple node groups with different instance types and scaling policies to handle diverse workload requirements. Use taints and tolerations to ensure appropriate workload placement.
# Create node group with specific instance types for CPU-intensive workloads
aws eks create-nodegroup \\
--cluster-name production-cluster \\
--nodegroup-name cpu-intensive-nodes \\
--instance-types c5.xlarge,c5.2xlarge \\
--ami-type AL2_x86_64 \\
--capacity-type SPOT \\
--scaling-config minSize=2,maxSize=10,desiredSize=3 \\
--taints key=workload-type,value=cpu-intensive,effect=NoSchedule
Monitor node utilization patterns and right-size instance types based on actual workload requirements. Consider using Spot instances for fault-tolerant workloads to reduce costs by up to 70%.
Configure Cluster-Level Monitoring and Logging
Why it matters: Without comprehensive observability, troubleshooting cluster issues becomes reactive and time-consuming. Proper monitoring enables proactive identification of performance bottlenecks, security threats, and resource constraints before they impact applications.
Implementation: Enable CloudWatch Container Insights and configure centralized logging for all cluster components. Set up custom metrics and alerts for key performance indicators specific to your applications.
resource "aws_cloudwatch_log_group" "eks_cluster_logs" {
name = "/aws/eks/production-cluster/cluster"
retention_in_days = 30
tags = {
Environment = "production"
Service = "eks-cluster"
}
}
# Enable cluster logging
resource "aws_eks_cluster" "production" {
name = "production-cluster"
role_arn = aws_iam_role.eks_cluster_role.arn
enabled_cluster_log_types = [
"api",
"audit",
"authenticator",
"controllerManager",
"scheduler"
]
depends_on = [
aws_cloudwatch_log_group.eks_cluster_logs
]
}
Configure log retention periods based on compliance requirements and set up automated log analysis to identify security anomalies or performance degradation patterns.
Implement Progressive Cluster Upgrades
Why it matters: Kubernetes releases new versions every 3-4 months, and AWS typically supports only the latest four versions. Falling behind on upgrades can lead to security vulnerabilities and loss of support for critical features.
Implementation: Establish a regular upgrade cadence that includes testing in non-production environments first. Plan upgrades during maintenance windows and maintain rollback procedures for emergency situations.
# Check current cluster version and available upgrades
aws eks describe-cluster --name production-cluster --query 'cluster.version'
# Update cluster control plane
aws eks update-cluster-version \\
--name production-cluster \\
--kubernetes-version 1.28 \\
--wait
# Update node groups after control plane upgrade
aws eks update-nodegroup-version \\
--cluster-name production-cluster \\
--nodegroup-name primary-nodes \\
--kubernetes-version 1.28 \\
--force
Test all applications thoroughly after upgrades and monitor for deprecated API usage that might break in future versions. Use tools like kubectl convert to update manifests to newer API versions proactively.
Optimize Cost Management and Resource Allocation
Why it matters: EKS costs can escalate quickly without proper resource management, especially when running continuously in development environments or when workloads are not properly right-sized.
Implementation: Implement automated scaling policies, use Spot instances where appropriate, and establish resource optimization practices that align with actual usage patterns.
# Configure cluster autoscaler for cost optimization
resource "kubernetes_deployment" "cluster_autoscaler" {
metadata {
name = "cluster-autoscaler"
namespace = "kube-system"
}
spec {
replicas = 1
selector {
match_labels = {
app = "cluster-autoscaler"
}
}
template {
metadata {
labels = {
app = "cluster-autoscaler"
}
}
spec {
container {
name = "cluster-autoscaler"
image = "k8s.gcr.io/autoscaling/cluster-autoscaler:v1.28.0"
command = [
"./cluster-autoscaler",
"--v=4",
"--stderrthreshold=info",
"--cloud-provider=aws",
"--skip-nodes-with-local-storage=false",
"--expander=least-waste",
"--scale-down-delay-after-add=10m",
"--scale-down-unneeded-time=10m"
]
env {
name = "AWS_REGION"
value = "us-west-2"
}
}
}
}
}
}
Schedule regular cost reviews and implement policies that automatically shut down non-production clusters outside business hours. Use AWS Cost Explorer to identify cost optimization opportunities and track spending trends over time.
Product Integration
Overmind Integration
EKS clusters are central components that connect to virtually every part of your AWS infrastructure. When you modify an EKS cluster, you're potentially affecting not just the Kubernetes workloads running inside it, but also the entire network topology, security boundaries, and service integrations that support your applications.
When you run overmind terraform plan with EKS cluster modifications, Overmind automatically identifies all resources that depend on your cluster configuration, including:
- Network Dependencies All VPC subnets, security groups, route tables, and NAT gateways that provide networking for your cluster
- IAM Resources Service roles, IAM policies, and instance profiles that grant necessary permissions to cluster components
- Compute Resources Launch templates, Auto Scaling groups, and EC2 instances that make up your node groups
- Storage and Logging EFS file systems for persistent storage and CloudWatch log groups for cluster logging
This dependency mapping extends beyond direct relationships to include indirect dependencies that might not be immediately obvious, such as Lambda functions that interact with your cluster through kubectl operations, RDS databases that serve as backends for applications running in the cluster, or S3 buckets that store application artifacts accessed by pods.
Risk Assessment
Overmind's risk analysis for EKS cluster changes focuses on several critical areas:
High-Risk Scenarios:
- Control Plane Version Changes: Kubernetes version upgrades can break compatibility with existing workloads and require careful coordination with application teams
- Network Configuration Changes: Modifying subnet configurations or security groups can disrupt pod networking and external connectivity
- IAM Role Modifications: Changes to cluster service roles or node group roles can prevent nodes from joining the cluster or accessing required AWS services
Medium-Risk Scenarios:
- Node Group Scaling: Increasing or decreasing node capacity affects application availability and resource allocation
- Addon Configuration: Modifying EKS addons like CoreDNS or kube-proxy can impact cluster functionality and application connectivity
Low-Risk Scenarios:
- Tag Updates: Adding or modifying resource tags typically has no functional impact on cluster operations
- Logging Configuration: Enabling or disabling CloudWatch logging affects observability but not cluster functionality
Use Cases
Enterprise Application Modernization
Organizations migrating from traditional monolithic applications to microservices architecture find EKS provides the scalability and reliability needed for production workloads. A financial services company might deploy their trading platform on EKS, taking advantage of the service's integration with Application Load Balancers for traffic distribution and KMS keys for encryption at rest and in transit.
The business impact includes reduced deployment times from weeks to hours, improved application scalability during peak trading periods, and enhanced security through AWS's shared responsibility model. This approach allows development teams to focus on business logic rather than infrastructure management, accelerating time-to-market for new features.
Data Processing and Analytics Workloads
EKS excels at running batch processing jobs and data analytics workloads that require dynamic scaling based on data volume and processing requirements. Data engineering teams can deploy Apache Spark clusters on EKS that automatically scale based on job queue depth, while integrating with S3 buckets for data storage and SQS queues for job coordination.
The business impact includes cost optimization through efficient resource utilization, reduced processing time through parallel execution, and improved data pipeline reliability through Kubernetes' self-healing capabilities. Organizations typically see 40-60% cost reductions compared to traditional always-on analytics infrastructure.
DevOps and CI/CD Pipeline Automation
Development teams use EKS as the foundation for continuous integration and deployment pipelines, running build agents, testing frameworks, and deployment tools as containerized workloads. The cluster can integrate with Lambda functions for trigger-based automation and SNS topics for notification systems.
The business impact includes faster deployment cycles, improved code quality through automated testing, and reduced operational overhead through infrastructure-as-code practices. Teams report 50-70% reduction in deployment failures and significantly faster recovery times when issues occur.
Limitations
Operational Complexity
While EKS eliminates control plane management, it introduces new operational challenges around node group management, addon configuration, and cluster networking. Teams need expertise in both Kubernetes and AWS services to operate EKS effectively. The learning curve can be steep for organizations new to container orchestration, and troubleshooting issues often requires deep understanding of both Kubernetes internals and AWS networking.
Cost Considerations
EKS charges $0.10 per hour for each cluster control plane, plus the cost of worker nodes and associated resources. For small workloads or development environments, this fixed cost can be significant compared to alternatives like ECS or Lambda. Additionally, running nodes continuously for unpredictable workloads can result in higher costs than serverless alternatives.
Vendor Lock-in and Portability
While Kubernetes itself is portable, EKS clusters rely heavily on AWS-specific services and features. Applications that use AWS Load Balancer Controller, EFS storage classes, or IAM roles for service accounts become tightly coupled to AWS infrastructure. Migrating these applications to other cloud providers or on-premises environments requires significant refactoring and architectural changes.
Conclusions
The EKS service is a sophisticated managed Kubernetes platform that balances operational simplicity with enterprise-grade reliability. It supports everything from simple web applications to complex data processing pipelines, providing the scalability and integration capabilities needed for modern cloud-native architectures. For organizations looking to adopt Kubernetes without the overhead of managing control planes, EKS offers all the functionality you might need.
EKS integrates seamlessly with the broader AWS ecosystem, from networking and security services to storage and monitoring solutions. However, you will most likely integrate your own custom applications with EKS as well. The interconnected nature of cluster components means that seemingly simple changes can have far-reaching impacts across your infrastructure.
This is where Overmind's dependency mapping and risk assessment become invaluable. By understanding the full scope of resources affected by EKS cluster changes, teams can make informed decisions about deployment timing, rollback strategies, and change coordination. The service's complexity means that proper planning and impact assessment are not just best practices—they're requirements for maintaining reliable production systems.