EKS vs GKE: A Production-Ready Decision Framework for Platform Teams

Your team just got the green light for Kubernetes, and now you’re staring at two titans: AWS EKS and Google GKE. Both promise managed Kubernetes nirvana. Both have impressive case studies. Both will happily take your money. But here’s what the marketing pages won’t tell you: the wrong choice doesn’t just mean suboptimal infrastructure—it means six months of migration work when you realize your networking model doesn’t fit, your IAM integration is fighting you at every turn, and your developers are writing more YAML workarounds than application code.

I’ve watched platform teams make this decision based on vibes, on which cloud their backend already runs on, or worse, on a single blog post comparison from 2019. And I’ve watched those same teams burn quarters of engineering time unwinding that choice.

The reality is that EKS and GKE have genuinely converged on core Kubernetes functionality. The control plane works. Nodes spin up. Pods get scheduled. Where they diverge—and where your decision actually matters—is in the integration layer: how you handle identity, how traffic flows, how storage attaches, and how your existing cloud investments compound or conflict with each choice.

This isn’t another feature checklist. It’s the decision framework we use with platform teams evaluating managed Kubernetes, complete with weighted scoring criteria you can adapt and Terraform examples that expose the real integration complexity. By the end, you’ll have a defensible recommendation for your organization, not just an opinion.

Let’s start with what’s actually at stake.

The Hidden Costs of Getting This Wrong

Choosing between Amazon EKS and Google Kubernetes Engine isn’t a reversible decision. Platform teams that underestimate migration complexity discover this the hard way—typically 3-6 months into a painful extraction process that consumes senior engineering bandwidth and stalls product roadmaps.

The control plane itself is the easy part. Kubernetes workloads are portable by design. But managed Kubernetes services earn their value through deep integration with their parent cloud’s ecosystem, and those integrations create the real lock-in.

Where Lock-in Actually Lives

Networking: Your VPC peering configurations, load balancer annotations, and ingress controller setups are cloud-specific. EKS clusters built around AWS Application Load Balancers and VPC CNI plugin configurations don’t translate to GKE’s native load balancing or Network Endpoint Groups.

Identity and Access Management: Workload identity bindings—IRSA for EKS, Workload Identity for GKE—embed cloud-specific IAM assumptions into your application code and Helm charts. A migration means rewriting every service account binding and updating deployment manifests across your entire fleet.

Storage: Persistent volume claims reference cloud-specific storage classes. Your EBS-backed StatefulSets won’t automatically migrate to Persistent Disks, and the performance characteristics differ enough to require capacity re-planning.

Observability: Teams running Container Insights or Cloud Operations for GKE have built dashboards, alerts, and runbooks around those platforms. Rebuilding operational knowledge takes longer than rebuilding infrastructure.

Pro Tip: Before comparing feature matrices, audit your existing cloud investments. Teams with mature AWS networking, IAM, and observability foundations face a steeper GKE learning curve than the Kubernetes layer suggests—and vice versa.

The Real Decision Criteria

The “right” choice depends less on which platform has better features and more on three factors: where your infrastructure already lives, what your team knows how to operate, and which cloud’s managed services your applications consume.

A startup running everything on GCP gains nothing from EKS’s AWS integrations. An enterprise with years of AWS networking expertise and a library of Terraform modules doesn’t need to relearn cloud fundamentals to run containers.

Understanding these dependencies sets the foundation for meaningful comparison. With the stakes clear, let’s examine how the control plane and node management approaches differ in practice.

Control Plane and Node Management: Where the Differences Matter

The control plane is where your Kubernetes investment either pays dividends or bleeds operational hours. Both EKS and GKE manage the control plane for you, but they diverge sharply in how much ongoing work they expect from your team.

Serverless Nodes: Two Different Philosophies

GKE Autopilot and EKS Fargate both promise “serverless” Kubernetes, but they deliver on that promise differently.

GKE Autopilot manages the entire node layer. You define pods, and Google handles node provisioning, scaling, security hardening, and OS patches. Autopilot enforces best practices by default—restricted pod security standards, required resource requests, and automatic bin-packing. You pay per pod resource, not per node.

EKS Fargate takes a more surgical approach. You define Fargate profiles that match specific namespaces or labels, and those pods run on AWS-managed infrastructure. Everything else still requires node groups you manage. This hybrid model offers flexibility but means you’re often running both Fargate pods and traditional nodes, doubling your operational surface.

Pro Tip: Autopilot works best for teams standardizing on a single operational model. Fargate shines when you need serverless for specific workloads (batch jobs, CI runners) while keeping traditional nodes for everything else.

Auto-Repair and Auto-Upgrade: Default-On vs Opt-In

GKE enables node auto-repair and auto-upgrade by default. Unhealthy nodes get replaced automatically. Minor and patch version upgrades happen during maintenance windows you configure. Your clusters stay current without intervention.

EKS takes the opposite stance. Managed node groups support update strategies, but you initiate upgrades explicitly. There’s no auto-repair for nodes—if a node becomes unhealthy, your monitoring needs to catch it, and your automation needs to handle replacement.

This philosophical difference extends to the control plane itself. GKE automatically upgrades control planes on a release channel schedule. EKS control planes require you to trigger upgrades, and you have approximately 14 months from a version’s release before it becomes unsupported.

For teams with limited platform engineering capacity, GKE’s automation reduces toil. For teams requiring strict change control, EKS’s explicit upgrade model integrates better with existing approval workflows.

Control Plane SLAs and Availability

Both platforms run highly available control planes across multiple zones within a region. GKE’s control plane SLA guarantees 99.95% uptime for zonal clusters and 99.99% for regional clusters. EKS provides a 99.95% SLA for the API server.

The practical difference lies in configuration. GKE regional clusters distribute control plane replicas across three zones automatically. EKS control planes are managed entirely by AWS—you don’t configure zone distribution, but you also can’t influence it.

Cluster Autoscaler Behavior

GKE’s cluster autoscaler integrates tightly with node pools, scaling nodes based on pending pods and configured utilization thresholds. Node auto-provisioning goes further, automatically creating new node pools with appropriate instance types for your workload requirements.

EKS relies on the Kubernetes Cluster Autoscaler or Karpenter, both requiring explicit installation and configuration. Karpenter, AWS’s newer node provisioner, offers faster scaling and more flexible instance selection but represents additional infrastructure to deploy and maintain.

The operational overhead gap is real: GKE provides autoscaling out of the box, while EKS requires deliberate setup and ongoing management of your scaling components.

Understanding these control plane differences establishes the foundation, but networking and security integration with your cloud provider’s IAM system is where day-to-day operational complexity truly lives.

Networking and Security: IAM Integration Deep Dive

The security posture of your Kubernetes cluster hinges on how workloads authenticate to cloud services. Both EKS and GKE have evolved sophisticated workload identity systems, but their implementation patterns differ significantly—and getting this wrong means either over-permissioned pods or authentication headaches that slow down every deployment.

Workload Identity: Two Approaches to the Same Problem

GKE’s Workload Identity creates a direct binding between Kubernetes service accounts and Google Cloud IAM service accounts. The mapping is explicit and declarative:

apiVersion: v1
kind: ServiceAccount
metadata:
  name: app-service-account
  namespace: production
  annotations:
    iam.gke.io/gcp-service-account: [email protected]

The corresponding IAM binding happens at the GCP level:

gcloud iam service-accounts add-iam-policy-binding \
  [email protected] \
  --role roles/iam.workloadIdentityUser \
  --member "serviceAccount:my-project.svc.id.goog[production/app-service-account]"

EKS Pod Identity (the successor to IRSA) simplifies the previous OIDC-based approach. You create an association that links the Kubernetes service account directly to an IAM role:

apiVersion: eks.amazonaws.com/v1alpha1
kind: PodIdentityAssociation
metadata:
  name: app-identity
  namespace: production
spec:
  serviceAccount: app-service-account
  roleArn: arn:aws:iam::123456789012:role/app-pod-role

Pro Tip: EKS Pod Identity requires the Pod Identity Agent addon. Install it before creating associations: aws eks create-addon --cluster-name my-cluster --addon-name eks-pod-identity-agent

The practical difference: GKE’s approach requires coordinating two resources (the annotation and the IAM binding), while EKS Pod Identity consolidates the relationship into a single association object. Both eliminate the need for long-lived credentials in your pods, replacing static secrets with short-lived tokens that are automatically rotated by the platform.

VPC-Native Networking and Pod IP Allocation

GKE’s VPC-native clusters use alias IP ranges, allocating pod IPs directly from your VPC’s secondary CIDR ranges. This enables native VPC routing to pods without overlay networks:

networkConfig:
  enableIntraNodeVisibility: true
  podRange: pods-range      # Secondary range: 10.4.0.0/14
  serviceRange: services-range  # Secondary range: 10.0.32.0/20

EKS uses the VPC CNI plugin, which assigns actual VPC IP addresses to pods from your subnet pools. This creates direct pod-to-pod communication across nodes but requires careful IP address planning:

apiVersion: v1
kind: ConfigMap
metadata:
  name: amazon-vpc-cni
  namespace: kube-system
data:
  enable-prefix-delegation: "true"  # Assigns /28 prefixes instead of individual IPs
  warm-prefix-target: "1"
  minimum-ip-target: "3"

Prefix delegation on EKS dramatically improves IP efficiency—a single ENI slot can support 16 pods instead of one. For clusters running at scale, this configuration change alone prevents subnet exhaustion. Without prefix delegation, large deployments frequently encounter IP exhaustion errors during scaling events, forcing emergency subnet expansions that disrupt ongoing operations.

Network Policy Enforcement

GKE offers native network policy enforcement through its built-in Dataplane V2 (powered by Cilium). This integration means network policies work out of the box without additional addon installation or configuration:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: api-ingress
  namespace: production
spec:
  podSelector:
    matchLabels:
      app: api-server
  policyTypes:
    - Ingress
  ingress:
    - from:
        - namespaceSelector:
            matchLabels:
              environment: production
      ports:
        - protocol: TCP
          port: 8080

EKS requires a CNI that supports network policies—typically Calico or Cilium installed as an addon. The policy syntax remains identical (standard Kubernetes NetworkPolicy), but the operational overhead of managing the policy engine falls on your platform team. This includes monitoring the policy controller’s health, managing upgrades independently of the cluster lifecycle, and troubleshooting policy enforcement failures that stem from CNI issues rather than policy misconfiguration.

When choosing a policy engine for EKS, consider that Calico provides robust L3/L4 policy enforcement with lower resource overhead, while Cilium offers advanced L7 visibility and eBPF-based observability at the cost of higher memory consumption on each node.

Private Cluster Configurations

Both platforms support fully private clusters, but the access control mechanisms differ:

GKE uses authorized networks to restrict control plane access:

privateClusterConfig:
  enablePrivateNodes: true
  enablePrivateEndpoint: true
  masterIpv4CidrBlock: 172.16.0.0/28
masterAuthorizedNetworksConfig:
  cidrBlocks:
    - cidrBlock: 10.0.0.0/8
      displayName: internal-network

EKS provides granular endpoint access controls:

vpcConfig:
  endpointPublicAccess: false
  endpointPrivateAccess: true
  publicAccessCidrs: []  # Empty when public access disabled

The security implications are clear: private endpoints eliminate attack surface but require VPN or Direct Connect/Cloud Interconnect for administrative access. Plan your CI/CD pipeline connectivity before committing to fully private configurations. Many teams discover post-deployment that their GitHub Actions runners or Jenkins agents cannot reach the API server, forcing last-minute network architecture changes.

For hybrid scenarios where you need occasional public access for debugging, EKS allows enabling both public and private endpoints simultaneously while restricting public access to specific CIDR blocks. GKE offers similar flexibility through its authorized networks configuration, letting you maintain a minimal public attack surface while preserving emergency access paths.

With identity and networking foundations established, the next step is codifying these patterns into reproducible infrastructure. The following section provides complete Terraform configurations for provisioning production-ready clusters on both platforms.

Terraform Configurations: Provisioning Both Platforms

Infrastructure as code transforms Kubernetes platform decisions from one-time choices into repeatable, auditable deployments. This section provides production-ready Terraform configurations for both EKS and GKE, highlighting the structural differences that affect your day-two operations.

Cluster Provisioning: The Foundation

The core cluster resources reveal fundamental architectural differences between platforms. EKS requires explicit IAM role creation and VPC configuration, while GKE abstracts more infrastructure concerns but exposes different configuration surfaces.

resource "aws_eks_cluster" "main" {
  name     = var.cluster_name
  version  = "1.29"
  role_arn = aws_iam_role.cluster.arn

  vpc_config {
    subnet_ids              = var.private_subnet_ids
    endpoint_private_access = true
    endpoint_public_access  = var.enable_public_endpoint
    security_group_ids      = [aws_security_group.cluster.id]
  }

  enabled_cluster_log_types = ["api", "audit", "authenticator"]

  encryption_config {
    provider {
      key_arn = aws_kms_key.cluster.arn
    }
    resources = ["secrets"]
  }

  depends_on = [
    aws_iam_role_policy_attachment.cluster_policy,
    aws_cloudwatch_log_group.cluster
  ]
}

resource "aws_eks_addon" "vpc_cni" {
  cluster_name  = aws_eks_cluster.main.name
  addon_name    = "vpc-cni"
  addon_version = "v1.16.0-eksbuild.1"

  configuration_values = jsonencode({
    enableNetworkPolicy = "true"
  })
}

resource "aws_eks_addon" "coredns" {
  cluster_name  = aws_eks_cluster.main.name
  addon_name    = "coredns"
  addon_version = "v1.11.1-eksbuild.6"
}

resource "google_container_cluster" "main" {
  name     = var.cluster_name
  location = var.region

  release_channel {
    channel = "REGULAR"
  }

  # Separate node pool management
  remove_default_node_pool = true
  initial_node_count       = 1

  network    = var.vpc_id
  subnetwork = var.subnet_id

  private_cluster_config {
    enable_private_nodes    = true
    enable_private_endpoint = !var.enable_public_endpoint
    master_ipv4_cidr_block  = "172.16.0.0/28"
  }

  workload_identity_config {
    workload_pool = "${var.project_id}.svc.id.goog"
  }

  cluster_autoscaling {
    enabled = true
    autoscaling_profile = "OPTIMIZE_UTILIZATION"
  }

  logging_config {
    enable_components = ["SYSTEM_COMPONENTS", "WORKLOADS"]
  }

  monitoring_config {
    enable_components = ["SYSTEM_COMPONENTS"]
    managed_prometheus {
      enabled = true
    }
  }
}

Notice how GKE’s release_channel replaces explicit version pinning, delegating upgrade decisions to Google’s tested release cadence. EKS requires you to manage version upgrades explicitly, providing more control but demanding more operational attention.

Node Pool Configuration: Where Complexity Lives

Node pools expose the sharpest differences in operational models. EKS managed node groups require separate IAM roles and launch templates, while GKE consolidates configuration within the node pool resource.

resource "aws_eks_node_group" "workers" {
  cluster_name    = aws_eks_cluster.main.name
  node_group_name = "${var.cluster_name}-workers"
  node_role_arn   = aws_iam_role.node.arn
  subnet_ids      = var.private_subnet_ids

  scaling_config {
    desired_size = var.node_desired_count
    max_size     = var.node_max_count
    min_size     = var.node_min_count
  }

  instance_types = ["m6i.xlarge", "m5.xlarge"]
  capacity_type  = "ON_DEMAND"

  update_config {
    max_unavailable_percentage = 25
  }

  labels = {
    workload = "general"
  }

  taint {
    key    = "dedicated"
    value  = "workers"
    effect = "NO_SCHEDULE"
  }

  launch_template {
    id      = aws_launch_template.workers.id
    version = aws_launch_template.workers.latest_version
  }
}

resource "aws_launch_template" "workers" {
  name_prefix = "${var.cluster_name}-workers"

  block_device_mappings {
    device_name = "/dev/xvda"
    ebs {
      volume_size           = 100
      volume_type           = "gp3"
      encrypted             = true
      kms_key_id            = aws_kms_key.cluster.arn
      delete_on_termination = true
    }
  }

  metadata_options {
    http_endpoint               = "enabled"
    http_tokens                 = "required"
    http_put_response_hop_limit = 1
  }
}

resource "google_container_node_pool" "workers" {
  name     = "${var.cluster_name}-workers"
  location = var.region
  cluster  = google_container_cluster.main.name

  autoscaling {
    min_node_count  = var.node_min_count
    max_node_count  = var.node_max_count
    location_policy = "BALANCED"
  }

  management {
    auto_repair  = true
    auto_upgrade = true
  }

  upgrade_settings {
    max_surge       = 1
    max_unavailable = 0
    strategy        = "SURGE"
  }

  node_config {
    machine_type = "n2-standard-4"
    disk_size_gb = 100
    disk_type    = "pd-ssd"

    oauth_scopes = [
      "https://www.googleapis.com/auth/cloud-platform"
    ]

    workload_metadata_config {
      mode = "GKE_METADATA"
    }

    shielded_instance_config {
      enable_secure_boot          = true
      enable_integrity_monitoring = true
    }

    labels = {
      workload = "general"
    }

    taint {
      key    = "dedicated"
      value  = "workers"
      effect = "NO_SCHEDULE"
    }
  }
}

Pro Tip: GKE’s auto_upgrade combined with release channels provides hands-off node upgrades. For EKS, implement a separate upgrade automation pipeline or accept manual node group updates as part of your maintenance window.

Add-on and Feature Flag Comparisons

Beyond the core cluster and node pool configurations, both platforms offer extensive add-on ecosystems that differ significantly in implementation approach. EKS treats add-ons as discrete, versioned components that you manage explicitly through the aws_eks_addon resource. This granular control means you can pin specific versions, delay updates during critical periods, and roll back problematic add-on releases independently of cluster upgrades.

GKE takes an integrated approach where many capabilities that EKS exposes as add-ons are instead feature flags within the cluster resource itself. Managed Prometheus, for instance, requires a single boolean toggle in GKE but demands separate installation and configuration in EKS—typically through the aws_eks_addon resource for the ADOT collector or a Helm-based deployment of the Prometheus operator.

Consider network policy support as a concrete example. In EKS, enabling network policies requires the VPC CNI add-on with explicit configuration values, plus potentially installing Calico for advanced policy features. GKE enables network policy enforcement through the network_policy block on the cluster resource, with Dataplane V2 providing native enforcement without additional components.

This architectural difference extends to cost visibility. GKE’s managed add-ons typically bundle their costs into the cluster management fee, while EKS add-ons may introduce additional compute requirements that appear in your EC2 billing. Teams running cost allocation should tag EKS add-on pods carefully to attribute their resource consumption accurately.

State Management for Multi-Cloud

Teams evaluating both platforms often run proof-of-concept deployments simultaneously. Structure your Terraform state to support this pattern without creating operational debt.

terraform {
  backend "s3" {
    bucket         = "platform-team-tfstate"
    key            = "kubernetes/production/terraform.tfstate"
    region         = "us-east-1"
    encrypt        = true
    dynamodb_table = "terraform-locks"
  }
}

## For GCP resources, use a separate workspace or state file
## to maintain clear blast radius boundaries

Separate state files per cloud provider prevent a misconfigured GKE change from locking your EKS deployment pipeline. Use Terraform workspaces or distinct state paths—never combine multi-cloud resources in a single state file.

For organizations committed to multi-cloud operations, consider a state management hierarchy that reflects your operational boundaries. A recommended pattern places shared resources (like DNS zones or central logging) in a dedicated state file, while each cluster maintains its own isolated state. This approach limits the blast radius of any single terraform apply while preserving the ability to reference cross-cutting resources through terraform_remote_state data sources.

The infrastructure code differences translate directly into operational burden. EKS configurations average 40% more lines of Terraform to achieve equivalent functionality, primarily due to explicit IAM and networking requirements. GKE’s opinionated defaults reduce initial configuration but require understanding which defaults to override for production hardening. Teams should factor this configuration complexity into their platform selection criteria, particularly when assessing the ongoing maintenance burden of infrastructure code reviews and updates.

With infrastructure provisioned, the true cost picture emerges from sustained operation rather than initial deployment. The next section breaks down the financial implications beyond compute pricing.

Cost Analysis: Beyond the Pricing Calculator

The sticker price of managed Kubernetes rarely reflects what you’ll actually pay. Platform teams who base their decisions solely on control plane costs discover this reality during their first invoice review. Building accurate cost projections requires understanding the full spectrum of charges across both platforms.

Control Plane: The Obvious Starting Point

EKS charges $0.10 per hour per cluster—$73 per month, $876 annually. This cost applies regardless of cluster size or workload. For organizations running multiple clusters across environments (dev, staging, production) and regions, this adds up quickly. Ten clusters means $8,760 in annual control plane costs before a single workload runs.

GKE Standard mode provides a free control plane for zonal clusters. Regional clusters, which provide higher availability, cost $0.10 per hour—matching EKS pricing. GKE Autopilot charges for pod resources rather than nodes, starting at $0.000014 per vCPU-second and $0.0000015 per GB-second for memory.

Data Transfer: The Silent Budget Killer

Cross-availability-zone traffic within a cluster generates charges that catch teams off guard. Both platforms charge for inter-zone data transfer, but the rates and patterns differ based on your networking architecture.

AWS charges $0.01 per GB for cross-AZ traffic in most regions. GKE charges vary by region, ranging from $0.01 to $0.02 per GB. For microservices architectures with heavy inter-service communication, these costs compound rapidly. A cluster processing 10TB of monthly inter-zone traffic adds $100-$200 to your bill.

Egress to the internet carries steeper penalties on both platforms. AWS and GCP use tiered pricing that decreases with volume, but the first TB often costs $0.09-$0.12 per GB.

Committed Use Discounts: Planning Ahead Pays Off

AWS Savings Plans offer up to 72% discount on EC2 instances with 1-year or 3-year commitments. These apply to EKS worker nodes running on EC2. Compute Savings Plans provide flexibility across instance families and regions.

GCP Committed Use Discounts deliver 57% savings for 3-year commitments on compute resources. Sustained use discounts automatically apply after 25% monthly usage, providing up to 30% savings without upfront commitments.

Pro Tip: GCP’s automatic sustained use discounts benefit teams with unpredictable workloads. AWS Savings Plans require more accurate forecasting but offer deeper discounts for stable baselines.

Hidden Infrastructure Costs

Logging and monitoring add substantial costs on both platforms. CloudWatch Logs ingestion costs $0.50 per GB; GCP Cloud Logging charges $0.50 per GB after the free tier. Load balancers carry hourly charges plus data processing fees—AWS ALB runs approximately $22 monthly before data charges, while GKE’s load balancers start at similar price points.

With cost structures clarified, the next step involves systematically scoring these factors against your specific requirements.

The Decision Matrix: Scoring Your Requirements

Selecting between EKS and GKE requires moving beyond feature comparisons to structured evaluation. A weighted scoring matrix transforms subjective preferences into quantifiable decisions that align with your organization’s specific constraints and priorities.

Building Your Weighted Criteria Framework

The framework evaluates four critical dimensions: ecosystem integration, operational complexity, total cost of ownership, and compliance requirements. Each dimension receives a weight based on organizational priorities, and each platform scores against specific criteria within that dimension.

from dataclasses import dataclass
from typing import Dict, List

@dataclass
class ScoringCriteria:
    name: str
    weight: float  # 0.0 to 1.0
    eks_score: int  # 1 to 5
    gke_score: int  # 1 to 5

def calculate_platform_scores(criteria: List[ScoringCriteria]) -> Dict[str, float]:
    """Calculate weighted scores for EKS and GKE based on organizational criteria."""
    eks_total = sum(c.weight * c.eks_score for c in criteria)
    gke_total = sum(c.weight * c.gke_score for c in criteria)
    max_possible = sum(c.weight * 5 for c in criteria)

    return {
        "eks": round((eks_total / max_possible) * 100, 1),
        "gke": round((gke_total / max_possible) * 100, 1),
        "recommendation": "EKS" if eks_total > gke_total else "GKE",
        "confidence": abs(eks_total - gke_total) / max_possible
    }

## Example: ML-focused startup with GCP data infrastructure
ml_startup_criteria = [
    ScoringCriteria("GPU/TPU availability", 0.25, 3, 5),
    ScoringCriteria("BigQuery integration", 0.20, 2, 5),
    ScoringCriteria("Autopilot operations", 0.15, 3, 5),
    ScoringCriteria("Multi-cluster mesh", 0.15, 3, 5),
    ScoringCriteria("Cost optimization", 0.15, 4, 4),
    ScoringCriteria("Team Kubernetes expertise", 0.10, 3, 4),
]

## Example: Enterprise with existing AWS investment
enterprise_aws_criteria = [
    ScoringCriteria("AWS service integration", 0.25, 5, 2),
    ScoringCriteria("IAM/SSO compatibility", 0.20, 5, 3),
    ScoringCriteria("Regulatory compliance (FedRAMP)", 0.20, 5, 4),
    ScoringCriteria("Existing Terraform modules", 0.15, 5, 3),
    ScoringCriteria("Support contract alignment", 0.10, 5, 3),
    ScoringCriteria("Team AWS expertise", 0.10, 5, 2),
]

print("ML Startup:", calculate_platform_scores(ml_startup_criteria))
print("Enterprise AWS:", calculate_platform_scores(enterprise_aws_criteria))

Running this produces clear recommendations: the ML startup scores GKE at 92.0% versus EKS at 58.7%, while the enterprise scenario favors EKS at 96.0% compared to GKE at 54.7%.

When GKE Wins

GKE emerges as the stronger choice for organizations running ML and AI workloads requiring TPU access or tight Vertex AI integration. Teams with deep Kubernetes expertise benefit from GKE’s adherence to upstream Kubernetes—new features arrive months earlier than on EKS. Organizations operating across multiple clusters gain significant value from Anthos and GKE Enterprise’s unified management plane, which simplifies policy enforcement and observability at scale.

When EKS Wins

EKS delivers superior value for organizations with substantial AWS investments. Native integration with over 200 AWS services through IAM Roles for Service Accounts eliminates credential management complexity. Enterprises in regulated industries benefit from EKS’s extensive compliance certifications and AWS GovCloud availability. Teams already operating production workloads on AWS find that existing VPC configurations, security groups, and monitoring pipelines extend naturally to EKS without architectural changes.

Hybrid Scenarios

Running workloads across both platforms makes sense in specific situations: acquisitions bringing different cloud footprints, genuine multi-cloud requirements for vendor diversification, or workload-specific optimization where certain applications perform measurably better on one platform. The operational overhead of maintaining expertise across both platforms justifies this approach only when business requirements demand it—not as a default architecture.

Pro Tip: Score your criteria independently before researching platform capabilities. This prevents confirmation bias from influencing your weights toward a predetermined outcome.

The scoring matrix provides a defensible recommendation, but implementation success depends on day-two operations. Planning for upgrades, incident response, and long-term maintenance separates successful platform adoptions from costly do-overs.

Migration Paths and Day-Two Operations

Choosing between EKS and GKE is a significant decision, but the real test comes after day one. Platform teams that plan for long-term operational success build abstractions and tooling choices that minimize lock-in while maximizing the strengths of their chosen platform.

GitOps: Your Platform-Agnostic Foundation

Flux and Argo CD operate identically on both EKS and GKE, making them the cornerstone of a portable platform strategy. By defining your entire cluster state—workloads, policies, and configurations—in Git, you create a deployment model that transcends provider differences. Your application manifests, Helm charts, and Kustomize overlays remain unchanged regardless of the underlying control plane.

This approach pays dividends during disaster recovery scenarios and multi-cluster expansions. Teams running Argo CD on EKS today can stand up a GKE cluster tomorrow and sync the same repository with minimal modifications to cluster-specific overlays.

Observability Stack Decisions

Both platforms offer native monitoring solutions: Amazon CloudWatch Container Insights and Google Cloud Managed Service for Prometheus. These integrate tightly with their respective ecosystems but create operational coupling.

Third-party stacks like Prometheus, Grafana, and OpenTelemetry provide portability at the cost of additional operational burden. The trade-off depends on your team’s priorities: native solutions reduce management overhead, while vendor-neutral tooling preserves flexibility.

Pro Tip: Standardize on OpenTelemetry for instrumentation regardless of your backend choice. This abstraction layer lets you switch observability platforms without modifying application code.

Version Management and Upgrades

EKS supports Kubernetes versions for 14 months after release, while GKE offers standard and rapid release channels with automated upgrades. Both enforce a version skew policy of n-2 for nodes relative to the control plane.

GKE’s release channels simplify upgrade planning but reduce control. EKS requires more manual orchestration but provides predictable maintenance windows. Build your upgrade runbooks around your team’s operational capacity rather than chasing the newest version.

Reducing Future Migration Risk

Abstract cloud-specific resources behind consistent interfaces. Use external-dns and cert-manager instead of provider-specific solutions. Define storage classes and ingress controllers through Kubernetes-native APIs. These patterns create a portable platform layer that makes future migrations—or multi-cloud expansions—achievable rather than aspirational.

With your operational strategy defined, the final step is scoring your specific requirements against each platform’s capabilities.

Key Takeaways

• Run the weighted decision matrix with your actual requirements before committing—ecosystem fit typically outweighs individual feature differences
• Implement workload identity (Pod Identity or Workload Identity) from day one to avoid retrofitting security later
• Budget for control plane costs on EKS ($73/month/cluster) and factor GKE Autopilot’s per-pod pricing into serverless workload calculations
• Adopt GitOps tooling like ArgoCD that abstracts platform differences, reducing future migration costs by up to 60%