AWS EKS AutoMode: A Comprehensive Guide

By Ananta Cloud Engineering Team | EKS | October 01, 2025

Amazon Elastic Kubernetes Service (EKS) simplifies Kubernetes management by providing a secure, scalable, and highly available control plane. Traditionally, users managed both the control plane and data plane (worker nodes) separately. With EKS AutoMode, AWS introduces a fully managed option that automates cluster compute provisioning, scaling, and management. This approach enables teams to focus more on applications instead of infrastructure management.

This blog, prepared by Ananta Cloud, explores EKS AutoMode in detail, including how it works, available options, step-by-step setup instructions, a visual workflow diagram, and best practices.

What is EKS AutoMode?

EKS AutoMode is a fully managed compute provisioning mode for Amazon EKS that automatically manages:

• Node provisioning

• Cluster scaling

• Pod scheduling

• Resource optimization

It eliminates the need to configure and maintain self-managed nodes or managed node groups, allowing Kubernetes clusters to dynamically scale with workload demands.

Key Features:

• Zero Node Management – No need to launch EC2 instances or manage Auto Scaling Groups.

• Pod-Level Scheduling – Kubernetes pods are scheduled directly onto AWS-managed compute capacity.

• Dynamic Scaling – Automatically scales pods up or down based on usage.

• Cost-Efficient – Pay only for the resources consumed, with AWS managing right-sizing and bin packing.

• Integration with AWS Ecosystem – Works seamlessly with AWS services such as CloudWatch, IAM, ALB Ingress Controller, and VPC networking.

  
  

How EKS AutoMode Works

When a pod is scheduled in EKS AutoMode:

1. The scheduler evaluates pod resource requests (CPU, memory, GPU, etc.).

2. EKS AutoMode provisions the right amount of compute capacity in the background.

3. Pods are launched without the need to bind them to a pre-existing node pool.

4. When pods terminate, the associated compute resources are released.

This model is similar to serverless Kubernetes, but with full Kubernetes API compatibility and ecosystem support.

Options in EKS AutoMode

EKS AutoMode provides multiple configuration and operational options, giving flexibility while maintaining simplicity.

01. Compute Types

EKS AutoMode supports multiple compute backends:

• EC2-backed AutoMode – Uses EC2 instances under the hood but fully managed by AWS.

• Fargate-backed AutoMode – Runs pods directly on AWS Fargate without provisioning servers.

• Hybrid Mode – Mix of EC2-managed nodes, AutoMode compute, and Fargate for specific workloads.

02. Pod-Level Customization

• Resource Requests & Limits – Define CPU, memory, and GPU requirements per pod.

• Taints & Tolerations – Control pod placement across AutoMode compute.

• NodeSelector/Topology Spread Constraints – Influence pod distribution.

03. Scaling Options

• Horizontal Pod Autoscaler (HPA) – Scales pods based on CPU/memory utilization.

• KEDA with EKS AutoMode – Supports event-driven autoscaling.

• Cluster Autoscaler (AWS-managed) – Adjusts compute capacity dynamically.

04. Networking Options

• VPC CNI Integration – Provides native pod networking with Amazon VPC.

• Security Groups for Pods – Apply fine-grained security controls.

• Private/Public Subnet Placement – Choose where AutoMode workloads run.

05. Storage Options

• Amazon EBS – Persistent volumes for stateful workloads.

• Amazon EFS – Shared file system for multiple pods.

• Amazon S3 via CSI Driver – Direct S3 integration.

06. Observability & Monitoring

• Amazon CloudWatch – Pod-level and cluster-level metrics.

• Prometheus & Grafana – Supported via AWS Managed Prometheus.

• AWS X-Ray – Distributed tracing for microservices.

07. Security Options

• IAM Roles for Service Accounts (IRSA) – Fine-grained pod IAM permissions.

• Encryption at Rest & In Transit – With AWS KMS.

• Pod Security Policies (PSPs)/OPA/Gatekeeper – Apply security policies.

Step-by-Step Guide to Create an EKS Cluster with AutoMode

Prerequisites

• AWS CLI installed and configured with proper IAM permissions.

• kubectl installed and configured.

• eksctl installed (optional but recommended).

• An AWS account with permissions to create VPC, EKS, and IAM resources.

Step 1: Enable AutoMode via AWS Console

1. Go to the EKS Console.

2. Click Create cluster.

3. Enter cluster name and select region.

4. Under Compute options, select EKS AutoMode.

5. Configure networking (choose VPC, subnets, and security groups).

6. Configure logging (CloudWatch integration recommended).

7. Click Create.

EKS will provision the control plane and enable AutoMode for compute.

Step 2: Enable AutoMode via AWS CLI

aws eks create-cluster \
--name my-eks-automode-cluster \
--region us-east-1 \
--kubernetes-version 1.30 \
--resources-vpc-config subnetIds=subnet-abc,subnet-def,securityGroupIds=sg-123 \
--compute-config mode=AUTO

Step 3: Connect kubectl

# Once the cluster is active:
aws eks update-kubeconfig --region us-east-1 --name my-eks-automode-cluster

Step 4: Deploy a Sample Application

# Deploy a simple Nginx app:
kubectl create deployment nginx --image=nginx
kubectl expose deployment nginx --port=80 --type=LoadBalancer

EKS AutoMode will provision compute resources and attach them to the load balancer.

Step 5: Validate AutoMode Scaling

# Scale the deployment:
kubectl scale deployment nginx --replicas=10
# Check pod status:
kubectl get pods -o wide

AWS AutoMode will automatically provision compute for new pods.

Step 6: Enable Autoscaling (Optional)

kubectl autoscale deployment nginx --cpu-percent=50 --min=1 --max=20

EKS AutoMode vs Traditional EKS

Feature	Traditional EKS (Managed Node Groups)	EKS AutoMode
Node Management	User-managed	AWS-managed
Scaling	Manual or via Cluster Autoscaler	Fully automatic
Cost	Pay for full EC2 instances	Pay for pod-level resources
Flexibility	Full control of nodes & OS	Limited but simplified
Best Fit	Advanced custom workloads	General workloads, serverless needs

Best Practices for EKS AutoMode

1. Use Pod Resource Requests Wisely – Over-provisioning can increase costs.

2. Leverage IRSA for secure pod-level IAM permissions.

3. Monitor with CloudWatch & Prometheus to optimize scaling.

4. Combine with Fargate for Bursty Workloads – Run spiky workloads on Fargate, steady workloads on EC2 AutoMode.

5. Integrate with KEDA for event-driven scaling beyond CPU/memory.

6. Use Cost Allocation Tags to track expenses at namespace, service, or team level.

When to Use EKS AutoMode

EKS AutoMode is ideal for:

• Startups/SMBs – Minimal ops burden, faster time to market.

• Event-driven Workloads – Scale workloads dynamically with KEDA.

• Multi-tenant Clusters – Simplified resource sharing.

• CI/CD Pipelines – On-demand build/test workloads.

• Dev/Test Environments – Avoid managing infra for short-lived clusters.

Limitations of EKS AutoMode

• Limited customization of underlying compute.

• May not suit latency-sensitive workloads that require dedicated hardware.

• Cost efficiency depends on right-sizing pod requests.

• Some advanced networking (e.g., custom CNI plugins) may have restrictions.

Conclusion

Amazon EKS AutoMode is a major step towards serverless Kubernetes on AWS, reducing infrastructure management overhead while providing flexibility at the pod level. For most modern applications, AutoMode offers the right balance between simplicity, scalability, and cost efficiency. However, teams with complex infrastructure requirements may still prefer traditional EKS with managed node groups.

At Ananta Cloud, we believe EKS AutoMode empowers organizations to build, scale, and secure Kubernetes workloads faster without infrastructure headaches.

If you are exploring Kubernetes adoption or migration to EKS AutoMode, our experts at Ananta Cloud can help you with best practices, cost optimization, and secure deployments.

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