EKS Auto vs GKE: Managed Kubernetes Comparison (2025)

• 20/8/2024
• 9-minute read

EKS Auto vs GKE: Managed Kubernetes Comparison (2025)

Which managed Kubernetes service is best for your cloud-native workloads? This guide compares AWS EKS Auto mode and Google Kubernetes Engine (GKE) for features, pricing, scalability, and operational simplicity—so you can make the right choice for your team.

Kubernetes is powerful but complex. EKS Auto and GKE both promise to simplify operations, but their approaches, pricing, and automation differ in ways that matter for real-world deployments.

Quick Comparison Table

Why Compare EKS Auto and GKE?

Both services aim to reduce Kubernetes management overhead, but their automation, pricing, and integration models are different. This article breaks down:

• How EKS Auto and GKE automate node and cluster management
• Pricing and cost optimization strategies
• Scalability and upgrade options
• Security, compliance, and ecosystem fit
• Real-world use cases and recommendations

EKS Auto vs GKE: Simplifying Kubernetes Management

Managing Kubernetes clusters has traditionally been a complex undertaking requiring specialized expertise. Cloud providers have responded by developing increasingly sophisticated managed Kubernetes services. Two leading contenders—AWS EKS Auto mode and Google Kubernetes Engine (GKE)—offer compelling approaches to simplify container orchestration. This article explores their key differences, advantages, and considerations for enterprises navigating the container ecosystem.

The Problem: Kubernetes Complexity at Scale

Kubernetes has become the de facto standard for container orchestration, but its complexity presents significant challenges:

• Resource provisioning and capacity planning
• Node management and scaling
• Control plane maintenance and upgrades
• Security configuration and compliance
• Cost optimization across varied workloads

Both AWS and Google Cloud have developed sophisticated solutions to address these challenges, each with distinct approaches reflecting their cloud philosophies.

AWS EKS Auto Mode: The New Contender

AWS recently introduced EKS Auto mode as an evolution of their Elastic Kubernetes Service. According to AWS Community resources, EKS Auto mode aims to simplify cluster management through intelligent automation.

Key Features of EKS Auto Mode

1. Automated Node Provisioning EKS Auto mode dynamically provisions and manages nodes based on your workload requirements. This eliminates the need to configure node groups or manually adjust capacity.

2. Intelligent Scheduling The service places pods based on their resource requirements, automatically scaling compute capacity when needed and optimizing pod placement across nodes.

3. Seamless Integration with AWS Services EKS Auto mode provides native integration with AWS services like IAM for authentication, VPC for networking, and CloudWatch for observability.

4. Simplified Cost Model With Auto mode, you pay for:

• The EKS control plane ($0.10 per hour per cluster)
• The actual compute resources consumed by your pods
• Associated storage and network resources

Here’s an example configuration for an EKS Auto mode cluster:

# EKS Auto mode cluster configuration example
apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig
metadata:
  name: eks-auto-cluster
  region: us-west-2
  version: '1.27'
autoScaling:
  enabled: true
  minNodes: 2
  maxNodes: 10
  scaleDownUtilizationThreshold: 0.5
  scaleUpUtilizationThreshold: 0.7

Google Kubernetes Engine: The Established Leader

Google Kubernetes Engine (GKE) was the first managed Kubernetes service from a major cloud provider, reflecting Google’s role in creating Kubernetes. GKE has continued to evolve with features focused on operational excellence and developer experience.

Key Features of GKE

1. Autopilot Mode GKE Autopilot represents Google’s fully managed Kubernetes experience. Similar to EKS Auto mode, it handles node provisioning and management automatically, but with years of optimization behind it.

2. Multi-Dimensional Auto-scaling GKE provides sophisticated auto-scaling across multiple dimensions:

• Horizontal Pod Autoscaling (HPA)
• Vertical Pod Autoscaling (VPA)
• Cluster Autoscaler for node scaling
• Node Auto-provisioning for node pool creation

3. Advanced Operational Features GKE includes features like:

• Release channels for controlled Kubernetes version management
• Built-in binary authorization and vulnerability scanning
• Workload identity for secure service authentication

4. GKE Enterprise For organizations requiring additional governance and multi-cluster management, GKE Enterprise (formerly Anthos) provides a comprehensive solution.

Here’s an example GKE Autopilot configuration:

# GKE Autopilot cluster creation example
gcloud container clusters create-auto gke-autopilot-cluster \
    --region=us-central1 \
    --release-channel=regular \
    --network=default \
    --enable-master-authorized-networks \
    --master-authorized-networks=203.0.113.0/24

Key Comparison Points

Let’s examine how these platforms compare across critical dimensions:

1. Node Management Approach

EKS Auto Mode:

• Manages node provisioning and scaling
• Supports multiple instance types to optimize costs
• Provides options for Spot instances to reduce costs
• Enables granular control over node configurations

GKE Autopilot:

• Fully abstracts node management
• Handles infrastructure optimization automatically
• Provides consistent per-pod pricing
• Focuses on workload requirements rather than infrastructure

2. Cost Structure

EKS Auto Mode:

• Control plane fee: $0.10/hour per cluster
• Compute costs based on the EC2 instances provisioned
• Potential for optimization using Spot instances and Graviton (ARM) processors
• Separate charges for data transfer and storage

GKE Autopilot:

• No separate control plane fee
• Simplified per-vCPU and per-GB memory pricing
• Automatic discounts for sustained use
• Commitment-based discounts available

This cost example illustrates a typical small production cluster:

# Monthly cost comparison for production cluster (rough estimates)
# EKS Auto Mode (us-east-1)
# - Control plane: $0.10/hr × 730 hours = $73
# - Compute: Average 8 nodes × $0.0416/hr (m6g.large) × 730 hours = $243
# - Total: ~$316/month

# GKE Autopilot (us-central1)
# - Pod resources: 32 vCPU × $0.0445/hr × 730 hours = $1,040
# - Memory: 128 GB × $0.0049/hr × 730 hours = $456
# - Total: ~$1,496/month

# Note: Actual costs vary widely based on workload patterns and optimization

3. Operational Overhead

EKS Auto Mode:

• Reduced node management burden compared to standard EKS
• Still requires some cluster configuration decisions
• AWS shared responsibility model applies

GKE Autopilot:

• Near-zero operational overhead
• Google manages all infrastructure components
• Focus entirely on application deployment

4. Feature Maturity

EKS Auto Mode:

• Newer offering with ongoing feature development
• Leverages AWS’s extensive service ecosystem
• Rapid iteration based on customer feedback

GKE Autopilot:

• Mature platform with years of production testing
• Deeper integration with Kubernetes (given Google’s leadership role)
• Advanced features from Google’s internal experience running containers at scale

Real-World Use Case: Microservices Platform

Consider a company building a microservices platform with varying workload characteristics:

1. API Services: Consistent, predictable load
2. Batch Processing: Periodic, resource-intensive jobs
3. Event Processing: Unpredictable, bursty workloads
4. Machine Learning: GPU-accelerated inference

EKS Auto Mode Approach

For this scenario, EKS Auto mode could be configured to:

• Utilize Graviton-based instances for API services for cost efficiency
• Scale batch processing on Spot instances to reduce costs
• Configure event processing with rapid scaling parameters
• Deploy ML workloads on GPU-optimized instances

The implementation leverages EKS’s flexibility while benefiting from automatic node provisioning:

# EKS Auto mode with mixed workload optimization
apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
  name: default
spec:
  template:
    spec:
      requirements:
        - key: "karpenter.sh/capacity-type"
          operator: In
          values: ["spot", "on-demand"]
        - key: "kubernetes.io/arch"
          operator: In
          values: ["amd64", "arm64"]
        - key: "node.kubernetes.io/instance-type"
          operator: In
          values: ["m6g.large", "c6g.large", "r6g.large", "g4dn.xlarge"]

GKE Autopilot Approach

With GKE Autopilot, the focus shifts entirely to workload specifications:

• Define resource requests and limits accurately for each service
• Use pod disruption budgets to ensure availability during scaling
• Implement HPA for each service with custom metrics
• Configure GPU resources directly in pod specifications

GKE Autopilot simplifies this with workload-focused configurations:

# GKE Autopilot pod specification example
apiVersion: apps/v1
kind: Deployment
metadata:
  name: api-service
spec:
  replicas: 5
  template:
    spec:
      containers:
      - name: api
        image: example/api:latest
        resources:
          requests:
            cpu: "500m"
            memory: "1Gi"
          limits:
            cpu: "2"
            memory: "2Gi"
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: api-service-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api-service
  minReplicas: 5
  maxReplicas: 50
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

Pros & Cons at a Glance

Actionable Takeaways: Which Should You Choose?

• Choose EKS Auto mode if:
  • You’re already invested in AWS
  • You want maximum flexibility and cost control (Spot, Graviton, custom node pools)
  • You have diverse or specialized workloads
• Choose GKE Autopilot if:
  • You want the simplest, most automated Kubernetes experience
  • You value Google’s K8s leadership and security defaults
  • You’re running standardized, cloud-native workloads

FAQ: EKS Auto vs GKE Autopilot

Q: Which is cheaper, EKS Auto or GKE Autopilot? A: EKS Auto can be cheaper for variable workloads using Spot and Graviton, but GKE Autopilot offers predictable pricing for standardized workloads. Always model your specific usage.

Q: Which is easier for beginners? A: GKE Autopilot is generally easier for Kubernetes newcomers due to its opinionated defaults and automation.

Q: Can I migrate workloads between EKS and GKE? A: Yes, but you’ll need to adapt IAM, networking, and some resource specs. Both are CNCF-conformant, but cloud-specific integrations differ.

Q: What about security? A: Both platforms offer strong security, but GKE Autopilot enforces more secure-by-default settings. EKS gives you more control, but also more responsibility.

Q: Where can I learn more about Kubernetes alternatives? A: See our kind vs k3d comparison and AI Code Security Tools Comparison for related insights.

Choosing Between EKS Auto and GKE

Your choice between EKS Auto mode and GKE should consider:

1. Existing Cloud Investment: If already heavily invested in AWS or Google Cloud, the native option typically provides better integration.

2. Cost Sensitivity: For pure cost optimization, EKS Auto mode with Graviton and Spot instances can deliver significant savings.

3. Operational Philosophy:

   • If you prefer more control with less operational burden, EKS Auto mode offers a balanced approach.
   • If you want maximum simplicity and are willing to pay for it, GKE Autopilot provides an almost serverless Kubernetes experience.

4. Workload Characteristics:

   • Diverse or specialized workloads may benefit from EKS Auto mode’s flexibility.
   • Standardized workloads may be more cost-effective on GKE Autopilot.

Conclusion

Both EKS Auto mode and GKE Autopilot represent significant steps toward making Kubernetes more accessible and operational for organizations of all sizes. EKS Auto mode offers a flexible approach with cost optimization opportunities, while GKE provides a mature, highly automated experience that minimizes operational overhead.

The gap between these services continues to narrow as both AWS and Google enhance their offerings. Organizations should evaluate both options based on their specific requirements, cloud strategy, and operational preferences. Regardless of which service you choose, the trend toward simplified, automated Kubernetes management is a win for development teams looking to focus on application delivery rather than infrastructure management.

Further Reading

• AWS EKS Pricing Guide
• GKE Pricing Details
• AWS EKS Compute Types Documentation
• GKE Autopilot Official Announcement
• Understanding EKS Auto Mode and Fargate