EKS vs AKS vs GKE: Real-World Decision Framework for Production Kubernetes

Your team just got approval for a new microservices platform, and leadership wants it on Kubernetes. You’ve narrowed it down to EKS, AKS, or GKE—but the marketing pages all promise the same thing. Managed control plane, automatic upgrades, seamless scaling, enterprise security. Pick your favorite shade of blue and sign the contract.

After migrating production clusters across all three platforms over the past four years, I’ve learned the differences that actually matter aren’t in the feature lists. They’re in the 3 AM pages when your ingress controller conflicts with the cloud provider’s load balancer implementation. They’re in the six-month roadmap derailed because your chosen platform handles node pool upgrades differently than you assumed. They’re in the budget review where data transfer costs somehow tripled your infrastructure spend.

The reality is that EKS, AKS, and GKE represent fundamentally different philosophies about what “managed Kubernetes” means. AWS gives you primitives and expects you to assemble them. Azure tries to integrate everything into a cohesive enterprise story. Google assumes you want Kubernetes to work the way Google runs it internally. None of these approaches is wrong—but one of them will cost your team significantly more time, money, and frustration than the others based on your specific context.

This isn’t a feature comparison matrix. It’s a decision framework built from production incidents, migration projects, and the hard-won knowledge of what questions you should be asking before you commit to a three-year cloud contract.

Let’s start with what the pricing calculators won’t tell you.

The Hidden Costs Nobody Mentions

Every managed Kubernetes pricing page tells the same story: a simple hourly rate for the control plane. What they don’t show you is the financial reality six months into production—when data transfer bills spike, your team burns hours on provider-specific configurations, and you realize the “free tier” came with expensive strings attached.

Control Plane: The Obvious Starting Point

GKE offers a genuinely free control plane for the standard tier. EKS and AKS both charge $0.10/hour ($73/month per cluster). For a single cluster, this difference is negligible. Scale to ten clusters across multiple environments and regions, and you’re looking at an $8,760 annual delta before running a single workload.

But control plane pricing is a distraction from where costs actually compound.

Data Transfer: The Silent Budget Killer

Cross-AZ traffic in EKS defaults to approximately $0.01/GB in each direction. A modest microservices deployment pushing 50TB monthly between availability zones generates $1,000 in transfer costs alone. GKE’s regional clusters route traffic more efficiently by default, and AKS offers free inter-AZ bandwidth within the same virtual network.

The differences become stark with egress. AWS charges $0.09/GB for internet egress after the first 100GB. Google Cloud starts at $0.12/GB but drops aggressively with committed use discounts. Azure sits between them with tiered pricing that favors consistent, predictable workloads.

💡 Pro Tip: Map your actual traffic patterns before choosing a provider. A write-heavy analytics workload with minimal egress favors different pricing than a CDN-origin architecture pushing terabytes to the internet.

Operational Overhead: Defaults Matter More Than Features

EKS ships minimal. No cluster autoscaler by default. No pod identity webhook. No managed add-ons unless you explicitly enable them. This design philosophy offers maximum flexibility at the cost of initial setup time—expect 2-4 hours configuring what GKE Autopilot provides out of the box.

AKS lands in the middle, bundling Azure Monitor integration and a functional RBAC model from the start. GKE Autopilot manages nodes entirely, eliminating node-level operational decisions but restricting low-level customization.

The engineering hours spent on initial configuration and ongoing maintenance dwarf control plane costs. A cluster requiring 8 hours of monthly operational attention at $150/hour senior engineer rates costs $14,400 annually in labor—nearly 200 times the control plane difference.

The Talent Equation

Your team’s existing cloud expertise compounds every other cost decision. An AWS-fluent team building on GKE faces a learning curve measured in months, not days. The hiring market tilts heavily toward AWS skills; finding GKE-specialized SREs takes longer and often costs more.

TCO calculations that ignore talent acquisition and retention model a fantasy, not your actual operating environment.

Understanding these hidden costs establishes the financial baseline. Now let’s examine what each platform demands to reach a production-ready state.

Cluster Bootstrap: From Zero to Production

Getting a Kubernetes cluster running takes minutes. Getting one production-ready takes significantly longer—and the gap between those two states varies dramatically across EKS, AKS, and GKE. Understanding each platform’s defaults, security posture, and operational patterns is essential before committing to a cloud provider for your container infrastructure.

CLI Quickstart: Three Paths to a Running Cluster

Each platform offers its own CLI tooling with different defaults and opinions about cluster architecture. The commands below represent production-oriented configurations, not the minimal examples found in quickstart guides.

## EKS with eksctl - most common path
eksctl create cluster \
  --name production-cluster \
  --region us-east-1 \
  --version 1.29 \
  --nodegroup-name primary-workers \
  --node-type m6i.xlarge \
  --nodes 3 \
  --nodes-min 2 \
  --nodes-max 10 \
  --managed \
  --asg-access \
  --external-dns-access \
  --full-ecr-access \
  --alb-ingress-access

## AKS - integrated Azure CLI experience
az aks create \
  --resource-group production-rg \
  --name production-cluster \
  --location eastus \
  --kubernetes-version 1.29 \
  --node-count 3 \
  --node-vm-size Standard_D4s_v3 \
  --enable-cluster-autoscaler \
  --min-count 2 \
  --max-count 10 \
  --enable-managed-identity \
  --network-plugin azure \
  --network-policy calico

## GKE - opinionated defaults out of the box
gcloud container clusters create production-cluster \
  --region us-central1 \
  --release-channel regular \
  --num-nodes 3 \
  --machine-type e2-standard-4 \
  --enable-autoscaling \
  --min-nodes 2 \
  --max-nodes 10 \
  --enable-autorepair \
  --enable-autoupgrade \
  --workload-pool=myproject-123456.svc.id.goog

GKE ships with autorepair and autoupgrade enabled by default. EKS requires explicit opt-in for managed node groups. AKS sits between—managed identity comes standard, but network policies require explicit enablement. These differences compound over time: what starts as a minor configuration gap becomes a significant operational burden when managing dozens of clusters.

Default Security Postures: What Needs Hardening

The out-of-box security varies significantly across platforms, and understanding these gaps before deployment prevents security incidents down the road.

EKS creates clusters with public API endpoints by default. Your first hardening task: restrict API access to specific CIDR blocks or move to private endpoints entirely. The control plane logging is disabled—enable it immediately for CloudWatch integration. Additionally, EKS does not enable envelope encryption for Kubernetes secrets by default; configure a KMS key for secrets encryption before storing any sensitive data.

AKS defaults to Azure CNI without network policies. The cluster identity model improved substantially with managed identities, but you still need to explicitly enable Defender for Containers and configure Azure Policy for pod security standards. Pay attention to the default node pool configuration—AKS creates nodes with public IPs unless you explicitly disable this behavior.

GKE offers the strongest defaults. Shielded GKE nodes, Workload Identity, and Binary Authorization are available without additional infrastructure. However, the default node service account has excessive permissions—create dedicated service accounts per workload. GKE also enables legacy metadata endpoints by default on older node images; verify your nodes use the metadata concealment feature.

💡 Pro Tip: All three platforms support private clusters, but the networking complexity differs. GKE Private Clusters require Cloud NAT for outbound connectivity. EKS private endpoints need careful VPC planning. AKS private clusters integrate cleanly with Azure Private Link but complicate Azure DevOps pipelines.

Node Pool Patterns That Scale

Production clusters need multiple node pools for workload isolation. A single node pool forces all workloads to compete for the same resources and prevents targeted optimization for specific compute requirements.

## Add GPU nodes for ML workloads
eksctl create nodegroup \
  --cluster production-cluster \
  --name gpu-workers \
  --node-type p3.2xlarge \
  --nodes 0 \
  --nodes-min 0 \
  --nodes-max 5 \
  --node-labels workload=gpu \
  --node-taints nvidia.com/gpu=true:NoSchedule

Separate node pools for system components, general workloads, and specialized compute (GPU, high-memory) prevents resource contention and enables targeted scaling policies. Consider creating dedicated pools for batch processing workloads that can tolerate spot/preemptible instances, reducing compute costs by 60-80% for fault-tolerant jobs.

Terraform: The Production Standard

CLI tools work for exploration. Production infrastructure belongs in Terraform, where changes are tracked, reviewed, and applied consistently across environments.

module "eks" {
  source  = "terraform-aws-modules/eks/aws"
  version = "~> 20.0"

  cluster_name    = "production-cluster"
  cluster_version = "1.29"

  vpc_id     = module.vpc.vpc_id
  subnet_ids = module.vpc.private_subnets

  cluster_endpoint_public_access  = false
  cluster_endpoint_private_access = true

  eks_managed_node_groups = {
    primary = {
      instance_types = ["m6i.xlarge"]
      min_size       = 2
      max_size       = 10
      desired_size   = 3
    }
  }

  enable_cluster_creator_admin_permissions = true
}

The terraform-aws-modules/eks module handles the complexity of IAM roles, security groups, and OIDC provider configuration. Similar modules exist for AKS (Azure/aks/azurerm) and GKE (terraform-google-modules/kubernetes-engine), each abstracting platform-specific complexity while maintaining consistency in your infrastructure codebase. When evaluating these modules, check the release frequency and issue response time—community-maintained modules vary in quality and support responsiveness.

Infrastructure as Code also enables GitOps workflows for cluster management. Changes flow through pull requests, automated validation catches misconfigurations before apply, and audit logs provide complete change history. This discipline becomes critical when operating multiple clusters across regions or environments.

With clusters running, the next challenge is keeping them sized appropriately. The autoscaling approaches across these platforms reveal fundamentally different philosophies about infrastructure management.

Autoscaling Showdown: Karpenter vs KEDA vs GKE Autopilot

Autoscaling determines whether your cluster responds to demand in seconds or minutes—and whether you’re paying for idle compute at 3 AM. Each platform takes a fundamentally different approach, and understanding these differences prevents architectural regret. The wrong choice here compounds over time: you’ll either wrestle with operational complexity you didn’t need, or find yourself constrained when workload requirements evolve.

EKS with Karpenter: Surgical Node Provisioning

Karpenter replaced the Kubernetes Cluster Autoscaler as the recommended approach for EKS. Instead of managing node groups with predefined instance types, Karpenter provisions nodes directly based on pending pod requirements—selecting optimal instance types, availability zones, and purchase options in real-time.

apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
  name: default
spec:
  template:
    spec:
      requirements:
        - key: kubernetes.io/arch
          operator: In
          values: ["amd64", "arm64"]
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["spot", "on-demand"]
        - key: karpenter.k8s.aws/instance-category
          operator: In
          values: ["c", "m", "r"]
      nodeClassRef:
        group: karpenter.k8s.aws
        kind: EC2NodeClass
        name: default
  limits:
    cpu: 1000
  disruption:
    consolidationPolicy: WhenEmptyOrUnderutilized
    consolidateAfter: 1m

Karpenter’s consolidation feature continuously right-sizes your cluster, replacing underutilized nodes with better-fitting instances. This aggressive optimization routinely delivers 30-40% cost savings compared to static node groups—but requires careful pod disruption budget configuration to avoid service interruptions during consolidation events.

The tradeoff is setup complexity. Karpenter requires IAM roles for service accounts, proper VPC subnet tagging, and understanding of EC2 instance type characteristics. Teams unfamiliar with AWS infrastructure spend significant time on initial configuration. Once running, however, Karpenter’s ability to mix Spot and on-demand instances across multiple instance families provides flexibility that static node groups cannot match.

💡 Pro Tip: Enable Karpenter’s drift detection to automatically replace nodes when your NodePool requirements change, eliminating manual node rotation during configuration updates.

AKS with KEDA: Event-Driven Scaling

Azure embraces KEDA (Kubernetes Event-Driven Autoscaling) as a first-class citizen. While Karpenter focuses on infrastructure-level scaling, KEDA scales workloads based on event sources—queue depth, HTTP requests, cron schedules, or custom metrics. This distinction matters: Karpenter answers “what nodes do I need?” while KEDA answers “how many pods should be running?”

apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: order-processor
  namespace: production
spec:
  scaleTargetRef:
    name: order-processor
  minReplicaCount: 2
  maxReplicaCount: 50
  triggers:
    - type: azure-servicebus
      metadata:
        queueName: orders
        namespace: myservicebus
        messageCount: "5"
      authenticationRef:
        name: servicebus-auth
    - type: cron
      metadata:
        timezone: America/New_York
        start: 0 8 * * 1-5
        end: 0 18 * * 1-5
        desiredReplicas: "10"

KEDA scales pods to zero during idle periods—impossible with standard Horizontal Pod Autoscaler. For event-driven architectures processing messages from Service Bus, Event Hubs, or Kafka, KEDA provides sub-second scaling response. AKS bundles KEDA as a managed add-on, eliminating version management overhead. Note that KEDA isn’t Azure-exclusive; it runs on any Kubernetes cluster, but Azure’s managed integration reduces operational friction significantly.

GKE Autopilot: Managed Simplicity

GKE Autopilot removes node management entirely. You deploy pods; Google provisions and manages the underlying infrastructure. No node pools, no capacity planning, no OS patching. For teams without dedicated platform engineers, this abstraction eliminates an entire category of operational concerns.

This simplicity comes with constraints. You cannot run privileged containers, DaemonSets require explicit allowlisting, and resource requests become billing units. Autopilot enforces minimum resource requests (250m CPU, 512Mi memory) that inflate costs for small utility workloads. Running dozens of lightweight sidecars or utility pods becomes disproportionately expensive compared to Standard mode.

For steady-state workloads with predictable resource patterns, Autopilot eliminates operational burden. For workloads requiring GPU scheduling, custom kernel modules, or aggressive bin-packing, Standard mode with custom node pools remains necessary. Evaluate your workload portfolio honestly before committing—migrating away from Autopilot later requires rearchitecting deployment manifests.

Matching Scaling Strategy to Workload

Choose Karpenter for heterogeneous workloads requiring diverse instance types, aggressive Spot usage, or ARM64 migration. The initial setup investment pays dividends for teams running varied workloads at scale. Choose KEDA for event-driven architectures where scale-to-zero matters or when scaling decisions depend on external metrics like queue depth. Choose Autopilot when operational simplicity outweighs fine-grained control and your workloads fit comfortably within its constraints.

These approaches aren’t mutually exclusive. Many AKS deployments combine KEDA for workload scaling with cluster autoscaler for node provisioning. EKS clusters can layer KEDA atop Karpenter for comprehensive scaling coverage.

The autoscaling decision cascades into identity management—particularly how scaling components authenticate with cloud APIs to provision resources and access secrets.

Identity and Access: The Integration Tax

Every managed Kubernetes platform promises seamless cloud identity integration. The reality involves navigating proprietary authentication mechanisms, understanding token exchange flows, and accepting varying degrees of vendor lock-in. Getting workload identity right determines whether your security posture scales with your cluster growth or becomes a maintenance burden.

EKS: Two Paths to Pod Identity

AWS offers two mechanisms for granting pods access to AWS services. IAM Roles for Service Accounts (IRSA) has been the standard since 2019, using OIDC federation to project tokens into pods. The newer EKS Pod Identity, launched in late 2023, simplifies the setup by eliminating OIDC provider configuration.

apiVersion: eks.amazonaws.com/v1alpha1
kind: PodIdentityAssociation
metadata:
  name: s3-access
  namespace: data-pipeline
spec:
  serviceAccountName: data-processor
  roleArn: arn:aws:iam::1234567890:role/DataProcessorRole

Pod Identity reduces the trust policy complexity that plagued IRSA deployments. You no longer need to maintain OIDC provider thumbprints or craft condition keys matching specific service account namespaces. The trade-off: Pod Identity requires the EKS Pod Identity Agent addon running as a DaemonSet, adding another component to your cluster management scope.

For new deployments, Pod Identity offers the cleaner path. For existing clusters with established IRSA patterns, migration requires updating trust policies and redeploying workloads—a non-trivial effort in production environments with hundreds of service accounts.

AKS and GKE: Converging on Federation

Azure’s Workload Identity and GKE’s Workload Identity Federation share architectural similarities. Both leverage federated identity tokens projected into pods, exchanged for cloud provider credentials at runtime.

AKS requires explicit namespace and service account annotations linking to Azure AD managed identities. GKE’s implementation feels more native, with Workload Identity Federation enabled at the cluster level and IAM bindings applied directly to Kubernetes service accounts.

The operational difference shows during troubleshooting. Azure’s multi-hop token exchange (Kubernetes → Azure AD → managed identity → resource) creates more potential failure points than GKE’s direct IAM binding model.

Cross-Platform Secrets with External Secrets Operator

Avoiding vendor lock-in for secrets management requires abstracting the retrieval layer. External Secrets Operator provides a consistent interface across all three platforms:

apiVersion: external-secrets.io/v1beta1
kind: ExternalSecret
metadata:
  name: database-credentials
  namespace: production
spec:
  refreshInterval: 1h
  secretStoreRef:
    name: cloud-secrets
    kind: ClusterSecretStore
  target:
    name: db-creds
  data:
    - secretKey: password
      remoteRef:
        key: prod/database/primary
        property: password

The SecretStore configuration changes per provider, but your application manifests remain portable. This abstraction adds operational overhead—another controller to maintain, another set of CRDs to version—but pays dividends when running workloads across multiple clouds or planning provider migrations.

💡 Pro Tip: Deploy External Secrets Operator with provider-specific SecretStores rather than a single ClusterSecretStore. This allows gradual migration between secret backends without cluster-wide changes.

The identity integration tax extends beyond initial setup. Token refresh intervals, credential caching behaviors, and audit logging capabilities differ significantly across platforms. These differences surface as production incidents when workloads exceed authentication rate limits or when security teams request compliance reports.

With identity foundations established, the next complexity layer emerges at the network boundary—where ingress controllers, service meshes, and load balancer integrations introduce their own platform-specific challenges.

Networking and Ingress: Where Complexity Lives

Kubernetes networking is where architectural decisions made during cluster setup come back to haunt you at 3 AM. Each managed platform handles pod networking differently, and understanding these differences prevents the IP exhaustion incidents and routing failures that plague production clusters.

EKS: The VPC CNI IP Address Trap

EKS uses the Amazon VPC CNI plugin by default, which assigns real VPC IP addresses to every pod. This provides excellent network performance and simplifies security group integration, but creates a critical constraint: your subnet sizing directly limits pod density.

A /24 subnet gives you 251 usable IPs. With the default CNI configuration, a c5.xlarge instance reserves 58 IP addresses (4 ENIs × 15 IPs each, minus the primary). Run five nodes and you’ve consumed your entire subnet before deploying a single application pod.

The fix requires planning during cluster creation. Use secondary CIDR blocks with the AWS_VPC_K8S_CNI_CUSTOM_NETWORK_CFG option, or enable prefix delegation to assign /28 prefixes instead of individual IPs—multiplying your available addresses by 16. Retrofitting these changes to running clusters requires node replacements and careful coordination.

💡 Pro Tip: Size your EKS subnets to /19 or larger for production clusters. The cost of unused IP space is negligible compared to emergency subnet expansion during an outage.

AKS: Choosing Between Azure CNI and Kubenet

AKS offers two networking modes with genuinely different operational profiles. Azure CNI assigns VNet IPs to pods (similar to EKS), while kubenet uses NAT and a private address space.

Kubenet works well for smaller clusters where pod-to-VNet direct communication isn’t required. Azure CNI becomes necessary when pods need to communicate directly with Azure services, on-premises networks via VPN, or when network policies require VNet-level enforcement. The hybrid option—Azure CNI Overlay—provides Azure CNI’s integration benefits with kubenet’s IP efficiency, and represents the best choice for most new deployments.

GKE: VPC-Native Done Right

GKE’s VPC-native clusters using alias IP ranges provide the cleanest networking model. Pods receive IP addresses from secondary ranges that don’t consume your node subnet space, and Google’s infrastructure handles the routing automatically. IP exhaustion issues are rare because you can allocate massive secondary ranges (/14 or larger) without affecting your broader network architecture.

The practical advantage: GKE networking requires the least ongoing attention. You configure it once during cluster creation and rarely think about it again.

Ingress and Load Balancer Integration

All three platforms offer native load balancer integration through cloud-specific ingress controllers. The pattern holds consistent: use the native controller (AWS Load Balancer Controller, Application Gateway Ingress Controller, GKE Ingress) for internet-facing traffic where cloud integration matters, and nginx-ingress or similar for internal services where portability matters more.

With networking foundations established, observability becomes the next operational challenge—particularly when managing multiple clusters across regions.

Multi-Cluster Operations and Observability

Running a single Kubernetes cluster is table stakes. Production environments demand multi-cluster architectures for regional redundancy, workload isolation, and blast radius containment. Each cloud provider approaches fleet management differently, and your choice here determines operational overhead for years to come.

EKS Dashboard and Multi-Cluster Visibility

AWS introduced the EKS Dashboard to address a long-standing gap: unified visibility across clusters scattered throughout your AWS organization. The dashboard aggregates cluster health, compliance status, and version information without requiring you to hop between regions or accounts.

Enable cross-account visibility by configuring resource sharing through AWS Organizations:

apiVersion: v1
kind: ConfigMap
metadata:
  name: aws-auth
  namespace: kube-system
data:
  mapRoles: |
    - rolearn: arn:aws:iam::1234567890:role/EKSObservabilityRole
      username: observability-reader
      groups:
        - system:authenticated
    - rolearn: arn:aws:iam::9876543210:role/CrossAccountEKSAccess
      username: cross-account-viewer
      groups:
        - eks-console-dashboard-full-access-group

The dashboard surfaces upgrade insights, flagging clusters running deprecated API versions before they break during upgrades. Combined with Amazon Managed Service for Prometheus, you get metrics aggregation across your fleet without managing Prometheus infrastructure.

Azure Arc: The Hybrid Play

Azure Arc extends Azure’s control plane to Kubernetes clusters running anywhere—on-premises, edge locations, or competing clouds. This approach resonates with organizations pursuing genuine multi-cloud strategies rather than marketing slides about multi-cloud.

Arc-enabled clusters gain Azure Policy enforcement, GitOps configuration through Flux, and centralized monitoring via Azure Monitor. The trade-off: you’re extending Microsoft’s management plane into infrastructure they don’t own, which creates its own dependency.

GKE Fleet Management and Config Sync

Google’s fleet management represents the most opinionated approach. GKE treats multiple clusters as a single logical unit, enabling policy enforcement and configuration drift detection across your entire fleet.

Config Sync implements GitOps at fleet scale:

apiVersion: configsync.gke.io/v1beta1
kind: RootSync
metadata:
  name: root-sync
  namespace: config-management-system
spec:
  sourceFormat: unstructured
  git:
    repo: https://github.com/acme-corp/platform-config
    branch: main
    dir: clusters/production
    auth: gcpserviceaccount
    gcpServiceAccountEmail: [email protected]

GKE’s Policy Controller, built on Open Policy Agent Gatekeeper, enforces guardrails across every cluster in your fleet. Define a constraint once; it applies everywhere.

Platform-Agnostic Observability

Vendor lock-in hits hardest in observability. Building on proprietary monitoring tools means rebuilding dashboards and alerts if you migrate. A platform-agnostic stack protects your investment:

Metrics: Prometheus with Thanos or Cortex for long-term storage and multi-cluster federation. Both support S3, GCS, and Azure Blob as backends.

Logging: Vector or Fluent Bit shipping to Loki, Elasticsearch, or your data platform of choice. Avoid cloud-native log solutions unless you’re committed to that vendor.

Tracing: OpenTelemetry collectors with Jaeger or Tempo backends. The OpenTelemetry Collector acts as your abstraction layer, letting you switch backends without touching application instrumentation.

💡 Pro Tip: Deploy your observability stack to a dedicated management cluster. This prevents a failing workload cluster from taking down your visibility into the failure.

The operational maturity of your multi-cluster strategy determines whether you spend your time fighting fires or preventing them. With fleet-wide visibility established, the final decision comes down to matching platform capabilities against your specific constraints.

The Decision Matrix: Choosing Your Platform

After examining bootstrap complexity, autoscaling strategies, identity integration, networking models, and multi-cluster operations, the patterns become clear. Each platform excels in specific contexts, and choosing wrong means fighting your infrastructure instead of building on it.

When EKS Wins

Choose EKS when your organization runs on AWS and needs deep ecosystem integration. EKS shines when your workloads depend on native AWS services—RDS, ElastiCache, SQS, Lambda—and you want IAM Roles for Service Accounts (IRSA) to handle authentication seamlessly.

Karpenter is the deciding factor for compute-intensive workloads. If your clusters scale aggressively with heterogeneous instance requirements, Karpenter’s just-in-time provisioning and consolidation capabilities outperform traditional cluster autoscalers. Organizations running batch processing, CI/CD workloads, or ML training jobs see 30-50% cost reductions.

EKS Anywhere extends this to hybrid scenarios. Financial services and healthcare organizations with on-premises requirements can run the same control plane across data centers and AWS regions, managed through a single pane of glass via the EKS Dashboard.

💡 Pro Tip: If you’re already running 50+ AWS services in production, EKS reduces cognitive overhead. Fighting the AWS ecosystem from another platform costs more than any perceived Kubernetes purity.

When AKS Wins

Choose AKS when Microsoft technologies dominate your stack. Azure Active Directory integration, Windows container support, and .NET workload optimization make AKS the natural choice for enterprises running Visual Studio, Azure DevOps, and SQL Server.

AKS handles Windows node pools better than competitors. If your architecture includes legacy .NET Framework applications alongside modern .NET 8 services, AKS provides a unified orchestration layer without virtualization hacks.

The Azure Arc story matters for organizations with existing Azure governance. Extending Azure Policy and RBAC across on-premises Kubernetes clusters through Arc creates consistency that EKS Anywhere and Anthos competitors struggle to match in Microsoft-centric environments.

When GKE Wins

Choose GKE when Kubernetes expertise runs deep and operational simplicity matters. GKE Autopilot eliminates node management entirely—Google handles scaling, security patching, and infrastructure optimization while you focus on workloads.

ML and AI workloads favor GKE. Native TPU support, tight Vertex AI integration, and Google’s networking backbone make GKE the performance leader for training and inference at scale.

Teams prioritizing Kubernetes portability benefit from GKE’s standards-first approach. Less proprietary tooling means easier multi-cloud strategies and reduced lock-in compared to AWS-native patterns.

Avoiding Lock-In Traps

Platform lock-in happens through services, not Kubernetes itself. IRSA, Workload Identity, and Azure AD Pod Identity all solve the same problem differently. Abstract your secrets management, use Crossplane or Terraform for infrastructure, and containerize your observability stack.

The migration path you plan today determines the flexibility you have tomorrow.

Key Takeaways

• Calculate TCO including data transfer, operational overhead, and team expertise—not just compute costs
• Start with managed node groups or Autopilot, then add Karpenter or custom node pools only when you hit specific scaling limitations
• Implement workload identity from day one using each platform’s native solution, but abstract secrets management with external-secrets operator
• Choose based on your existing cloud investment and team expertise—the Kubernetes layer is more portable than the ecosystem integrations