From AWS to GCP: A Practical Migration Guide for Kubernetes Workloads

Your organization just announced a multi-cloud strategy, and suddenly your battle-tested EKS clusters need GCP counterparts. The documentation assumes you’re starting fresh, but you’re not—you have years of AWS muscle memory and production patterns that need translation, not replacement.

You’ve spent months fine-tuning your EKS node groups, perfecting your IAM roles for service accounts, and building GitOps pipelines that deploy with surgical precision. Now someone in leadership read a Gartner report about vendor lock-in, and you’re staring at the GKE console wondering why nothing looks familiar. The concepts feel similar enough to be dangerous—just different enough to break your assumptions in production.

Here’s what the official migration guides won’t tell you: GCP isn’t trying to be AWS with different branding. Google built their cloud infrastructure around fundamentally different architectural principles, shaped by their internal tooling and global network. The patterns that made you successful on EKS—the way you think about accounts, networking, and identity—need recalibration, not just renaming.

This guide bridges that gap. Instead of treating you like a cloud novice, we’ll map your existing AWS knowledge directly to GCP equivalents, highlighting where the mental models align and where they diverge in ways that matter. You’ll learn to leverage GCP’s genuine advantages while avoiding the traps that catch experienced AWS engineers who assume too much similarity.

The first shift happens before you provision a single resource: understanding why GCP organizes everything around projects instead of accounts, and how that decision ripples through every architectural choice you’ll make.

The Mental Model Shift: AWS vs GCP Architecture Philosophy

Moving from AWS to GCP isn’t just about learning new service names—it requires rewiring how you think about cloud architecture. The fundamental organizational models differ significantly, and understanding these differences upfront saves hours of confusion when you start building infrastructure.

Resource Hierarchy: Projects vs Accounts

AWS organizes resources around accounts. Each account serves as both a security boundary and a billing container, leading many organizations to adopt multi-account strategies with AWS Organizations layered on top.

GCP inverts this model. Projects sit at the center of everything—they contain resources, define billing, and serve as the primary unit of deployment. Above projects, you’ll find folders (for grouping) and organizations (your root node tied to a Google Workspace or Cloud Identity domain). This hierarchy is built-in from day one, not bolted on afterward.

For Kubernetes teams, this distinction matters immediately: an EKS cluster lives in an AWS account, while a GKE cluster lives in a GCP project. When you plan multi-cluster architectures, you’re deciding between multiple projects (with separate quotas and IAM policies) versus a single project with multiple clusters.

IAM: Inheritance Changes Everything

AWS IAM policies attach directly to users, roles, or resources. Each policy explicitly grants permissions for specific actions on specific resources. The model is precise but verbose.

GCP IAM uses a hierarchical inheritance model. Permissions granted at the organization level cascade down through folders to projects to individual resources. You assign roles (collections of permissions) to principals (users, groups, or service accounts) at any level in the hierarchy.

💡 Pro Tip: GCP’s predefined roles like roles/container.clusterAdmin map cleanly to common Kubernetes operations. Start with these before building custom roles—they’re designed around actual workflow patterns.

The inheritance model means a single binding at the folder level can grant access to hundreds of projects. Powerful, but it demands careful planning to avoid over-permissioning.

Networking: The Global VPC Advantage

AWS VPCs are regional constructs. Subnets exist within a single availability zone, and cross-region connectivity requires explicit peering or Transit Gateway configurations.

GCP VPCs are global by default. A single VPC spans all regions, with subnets being regional (not zonal). This simplifies multi-region deployments significantly—your GKE clusters in different regions share a VPC without additional setup.

For teams running EKS across regions with complex VPC peering meshes, GCP’s global networking feels liberating. Your pod-to-pod communication across regions works within a single, flat network space.

Understanding these foundational differences prepares you for the practical work ahead. With the mental model in place, let’s translate your EKS expertise directly to GKE concepts and see where the platforms align—and where they diverge.

EKS to GKE: Translating Your Kubernetes Knowledge

Your EKS experience transfers directly to GKE—Kubernetes is Kubernetes. The differences lie in how each cloud implements the managed layer around your clusters. Understanding these translation points eliminates the learning curve and lets you leverage patterns you already know.

Node Pools vs Node Groups

EKS uses managed node groups backed by EC2 Auto Scaling groups. GKE uses node pools, which serve the same purpose but with tighter integration into the Kubernetes control plane.

## EKS managed node group
aws eks create-nodegroup \
  --cluster-name my-cluster \
  --nodegroup-name workers \
  --node-role arn:aws:iam::123456789012:role/eks-node-role \
  --scaling-config minSize=2,maxSize=10,desiredSize=3 \
  --instance-types t3.large

## GKE equivalent node pool
gcloud container node-pools create workers \
  --cluster=my-cluster \
  --zone=us-central1-a \
  --machine-type=e2-standard-4 \
  --num-nodes=3 \
  --enable-autoscaling \
  --min-nodes=2 \
  --max-nodes=10

GKE’s cluster autoscaler is built directly into the control plane rather than running as a separate deployment. This means faster scaling decisions and no additional configuration for the autoscaler itself. The autoscaler responds to pending pods within seconds rather than the minute-plus latency typical with the standalone Cluster Autoscaler deployment on EKS.

Node pool operations also differ in upgrade behavior. GKE supports surge upgrades and blue-green node pool strategies natively, allowing you to specify how many additional nodes can be created during upgrades and how many nodes can be unavailable simultaneously.

Workload Identity vs EKS Pod Identity

Both clouds solve the same problem: giving pods secure access to cloud APIs without embedding credentials. The implementation patterns are nearly identical.

## Create GCP service account
gcloud iam service-accounts create app-sa \
  --project=my-project

## Bind Kubernetes SA to GCP SA
gcloud iam service-accounts add-iam-policy-binding [email protected] \
  --role=roles/iam.workloadIdentityUser \
  --member="serviceAccount:my-project.svc.id.goog[default/app-ksa]"

## Annotate Kubernetes service account
kubectl annotate serviceaccount app-ksa \
  iam.gke.io/[email protected]

The key difference: EKS Pod Identity uses OIDC federation with IAM roles, while GKE Workload Identity binds Kubernetes service accounts directly to GCP service accounts. GKE’s approach requires fewer moving parts—no OIDC provider configuration or trust policy management.

💡 Pro Tip: Enable Workload Identity at cluster creation time. Retrofitting it to existing clusters requires node pool recreation.

GKE Autopilot vs Fargate

Fargate on EKS provisions compute per-pod. GKE Autopilot manages entire node pools automatically, handling machine types, scaling, and security hardening.

Autopilot charges per pod resource request, similar to Fargate. The difference: Autopilot still runs actual nodes (you just don’t manage them), which means DaemonSets work normally and you get better bin-packing efficiency. Fargate’s per-pod isolation model prevents DaemonSets entirely, forcing you to rearchitect logging and monitoring agents as sidecars.

Autopilot also enforces security best practices by default—containers run as non-root, privilege escalation is blocked, and host namespaces are inaccessible. These guardrails match what security-conscious teams implement manually on Standard clusters.

Choose Autopilot when you want zero node management overhead. Choose Standard mode when you need GPU workloads, specific machine types, or node-level customization.

Control Plane Differences

GKE’s control plane runs on Google-managed infrastructure with automatic updates enabled by default. EKS gives you more control over upgrade timing but requires explicit version management.

The operational impact: GKE clusters stay current automatically within your configured maintenance windows. EKS clusters need scheduled upgrade pipelines. For multi-cloud setups, standardize on explicit version pinning across both platforms to maintain consistency.

GKE also exposes control plane logs directly to Cloud Logging without additional configuration—no need to set up CloudWatch log groups or manage log retention separately. Audit logs, authenticator logs, and scheduler logs flow automatically once enabled.

## View API server audit logs
gcloud logging read 'resource.type="k8s_cluster" AND logName:"cloudaudit.googleapis.com"' \
  --project=my-project \
  --limit=50

One notable difference: GKE provides regional clusters with a multi-zone control plane by default in most configurations, offering higher availability than EKS’s single-endpoint design. The control plane automatically fails over across zones without intervention.

Both platforms deliver production-grade Kubernetes. The translation exercise is learning which knobs live where, not relearning container orchestration.

With your cluster running on GKE, the next step is codifying this infrastructure. Terraform patterns for GCP follow familiar conventions with provider-specific nuances worth understanding upfront.

Infrastructure as Code: Terraform Patterns for GCP

If you’ve been managing EKS clusters with Terraform, you’ll find the transition to GKE surprisingly smooth. The provider patterns differ, but the declarative infrastructure philosophy remains identical. Let’s translate your existing IaC expertise to GCP.

GCP Provider Configuration

The first difference you’ll encounter is authentication. While AWS uses IAM roles and access keys, GCP relies on service accounts and Application Default Credentials (ADC). This distinction affects how you’ll structure your provider blocks and manage credentials across environments.

terraform {
  required_version = ">= 1.5.0"

  required_providers {
    google = {
      source  = "hashicorp/google"
      version = "~> 5.0"
    }
    google-beta = {
      source  = "hashicorp/google-beta"
      version = "~> 5.0"
    }
  }

  backend "gcs" {
    bucket = "mycompany-terraform-state"
    prefix = "gke/production"
  }
}

provider "google" {
  project = var.project_id
  region  = var.region
}

provider "google-beta" {
  project = var.project_id
  region  = var.region
}

Notice the dual provider pattern. GCP’s beta features—often essential for production Kubernetes—require the google-beta provider. This differs from AWS, where preview features typically share the main provider. You’ll reference google-beta explicitly on resources that need cutting-edge capabilities like advanced networking options or newer security features.

💡 Pro Tip: Use GOOGLE_APPLICATION_CREDENTIALS environment variable pointing to your service account key for local development. In CI/CD, leverage Workload Identity Federation to eliminate long-lived credentials entirely—the GCP equivalent of IAM Roles for Service Accounts.

Production-Ready GKE Cluster

Here’s a battle-tested GKE configuration that mirrors the security posture you’d expect from a well-configured EKS cluster. Pay attention to the comments explaining how each setting maps to concepts you already understand from AWS:

resource "google_container_cluster" "primary" {
  provider = google-beta

  name     = "${var.environment}-cluster"
  location = var.region

  # Regional cluster for high availability (similar to EKS multi-AZ)
  node_locations = [
    "${var.region}-a",
    "${var.region}-b",
    "${var.region}-c"
  ]

  # Start with no default node pool—we'll create a managed one
  remove_default_node_pool = true
  initial_node_count       = 1

  # VPC-native cluster (required for private GKE)
  network    = google_compute_network.vpc.name
  subnetwork = google_compute_subnetwork.gke.name

  networking_mode = "VPC_NATIVE"
  ip_allocation_policy {
    cluster_secondary_range_name  = "pods"
    services_secondary_range_name = "services"
  }

  # Private cluster configuration
  private_cluster_config {
    enable_private_nodes    = true
    enable_private_endpoint = false
    master_ipv4_cidr_block  = "172.16.0.0/28"
  }

  # Workload Identity—the GCP equivalent of IRSA
  workload_identity_config {
    workload_pool = "${var.project_id}.svc.id.goog"
  }

  # Binary Authorization for supply chain security
  binary_authorization {
    evaluation_mode = "PROJECT_SINGLETON_POLICY_ENFORCE"
  }

  release_channel {
    channel = "REGULAR"
  }
}

resource "google_container_node_pool" "primary" {
  name       = "primary-pool"
  location   = var.region
  cluster    = google_container_cluster.primary.name
  node_count = var.nodes_per_zone

  node_config {
    machine_type = "e2-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
    }
  }

  autoscaling {
    min_node_count = 1
    max_node_count = 10
  }
}

The remove_default_node_pool pattern deserves special attention. GKE creates a default node pool that lacks the fine-grained configuration options you need for production workloads. By removing it and creating a separately managed pool, you gain full control over machine types, disk configurations, and security settings.

Network Configuration for Private Clusters

VPC-native clusters in GKE use alias IP ranges, providing better network performance than the kubenet networking you might remember from older EKS configurations. This approach assigns pod and service IPs directly from your VPC, enabling seamless communication with other GCP resources without NAT overhead.

resource "google_compute_network" "vpc" {
  name                    = "${var.environment}-vpc"
  auto_create_subnetworks = false
}

resource "google_compute_subnetwork" "gke" {
  name          = "${var.environment}-gke-subnet"
  ip_cidr_range = "10.0.0.0/20"
  region        = var.region
  network       = google_compute_network.vpc.id

  secondary_ip_range {
    range_name    = "pods"
    ip_cidr_range = "10.48.0.0/14"
  }

  secondary_ip_range {
    range_name    = "services"
    ip_cidr_range = "10.52.0.0/20"
  }

  private_ip_google_access = true
}

The secondary IP ranges deserve careful planning. The /14 range for pods accommodates approximately 250,000 pod IPs—sufficient for most production clusters. Size these ranges based on your expected pod density and growth projections. Unlike AWS where you might configure custom CNI settings, GKE handles this natively when you specify VPC_NATIVE networking mode.

Enabling private_ip_google_access allows nodes without external IPs to reach Google APIs and services through internal routing. This is essential for private clusters that need to pull container images from Artifact Registry or interact with Cloud Storage.

Multi-Environment Management

For managing development, staging, and production environments, combine Terraform workspaces with a modular structure. This approach provides isolation between environments while maintaining consistency through shared module definitions:

module "gke" {
  source = "../../modules/gke"

  project_id     = "mycompany-prod-12345"
  environment    = "production"
  region         = "us-central1"
  nodes_per_zone = 3

  # Production-specific overrides
  min_node_count = 2
  max_node_count = 20
  machine_type   = "e2-standard-8"
}

This mirrors the pattern many teams use with EKS—separate state files per environment, shared modules for consistency. Each environment maintains its own backend configuration, preventing accidental cross-environment modifications while enabling code reuse.

Consider implementing additional safeguards for production environments: require manual approval for terraform apply, integrate with your CI/CD pipeline’s protected environment features, and enable detailed audit logging through Cloud Audit Logs.

With your GKE infrastructure defined in code, you’re ready to tackle the next challenge: migrating your actual workloads. The cluster is just the foundation—let’s move your applications.

Workload Migration: Moving Applications to GKE

The actual workload migration requires systematic adaptation rather than wholesale rewrites. Your containers remain unchanged—the orchestration layer and supporting services need translation. This phase demands attention to detail: while the concepts map cleanly between platforms, the implementation specifics differ enough to cause subtle failures if overlooked.

Container Registry to Artifact Registry

GCP’s Artifact Registry replaces both ECR and the deprecated Container Registry, offering multi-format support for Docker images, language packages, and OS packages in a unified service. The migration path involves re-tagging and pushing images to your new repository:

steps:
  - name: 'gcr.io/cloud-builders/docker'
    args: ['pull', '123456789012.dkr.ecr.us-east-1.amazonaws.com/my-app:v1.2.3']

  - name: 'gcr.io/cloud-builders/docker'
    args: ['tag',
           '123456789012.dkr.ecr.us-east-1.amazonaws.com/my-app:v1.2.3',
           'us-central1-docker.pkg.dev/my-project/containers/my-app:v1.2.3']

  - name: 'gcr.io/cloud-builders/docker'
    args: ['push', 'us-central1-docker.pkg.dev/my-project/containers/my-app:v1.2.3']

Configure Workload Identity for pull authentication instead of managing static credentials. GKE nodes authenticate automatically when the Kubernetes service account binds to a GCP service account with Artifact Registry Reader permissions. This eliminates the image pull secrets you managed on EKS—one less credential rotation to track.

For large-scale migrations, consider using crane or skopeo for efficient multi-architecture image copies without pulling to an intermediate host. These tools preserve image digests, which matters if you’re using digest-based references in your deployments for immutability guarantees.

Adapting Helm Charts

Most Helm charts require minimal modification. Focus on these GKE-specific adaptations:

image:
  repository: us-central1-docker.pkg.dev/my-project/containers/my-app
  pullPolicy: IfNotPresent

serviceAccount:
  annotations:
    iam.gke.io/gcp-service-account: [email protected]

resources:
  requests:
    cpu: 250m
    memory: 512Mi
    ephemeral-storage: 1Gi

nodeSelector:
  cloud.google.com/gke-nodepool: application-pool

GKE’s node auto-provisioning respects resource requests more aggressively than Karpenter’s default behavior. Specify ephemeral-storage requests explicitly—GKE factors this into scheduling decisions. Pods without ephemeral storage requests may land on nodes with insufficient scratch space, causing container runtime failures that surface as cryptic eviction events.

Review your existing charts for AWS-specific assumptions: IAM role annotations need translation to Workload Identity bindings, EBS storage classes become Persistent Disk classes, and any hardcoded ARNs require replacement with GCP resource paths.

Ingress Controllers: Pattern Translation

The AWS ALB Controller and GKE Ingress serve similar purposes with different annotation vocabularies:

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: my-app-ingress
  annotations:
    kubernetes.io/ingress.class: "gce"
    kubernetes.io/ingress.global-static-ip-name: "my-app-ip"
    networking.gke.io/managed-certificates: "my-app-cert"
    kubernetes.io/ingress.allow-http: "false"
spec:
  rules:
    - host: app.example.com
      http:
        paths:
          - path: /api/*
            pathType: ImplementationSpecific
            backend:
              service:
                name: api-service
                port:
                  number: 8080

💡 Pro Tip: GKE Ingress uses ImplementationSpecific path type with glob patterns, while ALB Controller uses Prefix. Test path matching behavior thoroughly during migration—subtle differences cause routing failures.

For teams requiring NGINX or Istio, GKE supports these controllers without modification. The native GKE Ingress integrates tightly with Cloud Armor for WAF capabilities and Cloud CDN for edge caching. If you’re using AWS WAF rules extensively, map these to Cloud Armor policies before cutover—the rule syntax differs, but the security primitives align closely.

Secrets Management with Secret Manager

Replace AWS Secrets Manager references with GCP Secret Manager using the CSI driver:

apiVersion: secrets-store.csi.x-k8s.io/v1
kind: SecretProviderClass
metadata:
  name: my-app-secrets
spec:
  provider: gcp
  parameters:
    secrets: |
      - resourceName: "projects/my-project/secrets/database-password/versions/latest"
        path: "db-password"
      - resourceName: "projects/my-project/secrets/api-key/versions/latest"
        path: "api-key"
  secretObjects:
    - secretName: my-app-secrets
      type: Opaque
      data:
        - objectName: db-password
          key: DATABASE_PASSWORD

Mount secrets as volumes or sync them to Kubernetes secrets for environment variable injection. The CSI driver handles rotation automatically when you reference versions/latest. For production workloads requiring explicit version control, pin to specific version numbers and update through your deployment pipeline.

Workload Identity eliminates the service account key management burden. Your pods authenticate to Secret Manager through their bound GCP service account, maintaining the principle of least privilege without credential distribution. This authentication model extends to all GCP APIs—your application code can use the default credentials chain without modification.

With your applications running on GKE, you need visibility into their behavior. The observability story on GCP centers on Cloud Operations Suite, which provides integrated monitoring, logging, and tracing with minimal configuration overhead.

Observability and Operations on GCP

Moving from CloudWatch to GCP’s observability stack requires understanding a fundamental architectural difference: GCP treats monitoring, logging, and alerting as deeply integrated services rather than separate products. Cloud Operations Suite (formerly Stackdriver) provides a unified experience that maps well to AWS concepts once you understand the translation layer. This integration means less context-switching between consoles and more cohesive troubleshooting workflows.

Cloud Monitoring vs CloudWatch

CloudWatch dashboards translate directly to Cloud Monitoring dashboards, but the query language differs significantly. Where CloudWatch uses metric math and namespaces, Cloud Monitoring uses Monitoring Query Language (MQL) or PromQL. MQL provides more expressive aggregation capabilities, while PromQL support means existing Grafana dashboards can migrate with minimal changes.

## Alert policy for GKE node CPU utilization
displayName: "GKE Node High CPU"
combiner: OR
conditions:
  - displayName: "CPU utilization above 80%"
    conditionThreshold:
      filter: >
        resource.type="k8s_node"
        AND resource.labels.cluster_name="production-cluster"
        AND metric.type="kubernetes.io/node/cpu/allocatable_utilization"
      comparison: COMPARISON_GT
      thresholdValue: 0.8
      duration: "300s"
      aggregations:
        - alignmentPeriod: "60s"
          perSeriesAligner: ALIGN_MEAN
notificationChannels:
  - projects/my-project-id/notificationChannels/12345678

The filter syntax replaces CloudWatch’s namespace/metric name pattern. Resource types like k8s_node, k8s_container, and k8s_pod provide GKE-specific telemetry out of the box—no agents required. Alert policies support multiple notification channels including PagerDuty, Slack, and custom webhooks, matching the flexibility you expect from CloudWatch Alarms with SNS integration.

GKE Managed Prometheus

GKE includes Google Cloud Managed Service for Prometheus, eliminating the operational burden of running Prometheus yourself. If your EKS workloads already expose Prometheus metrics, migration is straightforward:

apiVersion: monitoring.googleapis.com/v1
kind: PodMonitoring
metadata:
  name: app-metrics
  namespace: production
spec:
  selector:
    matchLabels:
      app: api-server
  endpoints:
    - port: metrics
      interval: 30s
      path: /metrics
  targetLabels:
    fromPod:
      - from: app
        to: application

This PodMonitoring resource replaces ServiceMonitor CRDs from the Prometheus Operator. Your existing /metrics endpoints work unchanged—only the scrape configuration differs. The managed service handles storage, high availability, and long-term retention automatically, scaling with your cluster without capacity planning.

💡 Pro Tip: Enable Managed Collection in your GKE cluster to automatically scrape kubelet, kube-state-metrics, and node-exporter without deploying additional components. This provides comprehensive cluster health metrics with zero configuration overhead.

Log-Based Metrics and Analysis

Cloud Logging automatically ingests all GKE logs, including container stdout/stderr, system components, and audit logs. Creating log-based metrics—equivalent to CloudWatch Logs Insights with metric filters—enables alerting on application-specific patterns:

filter: >
  resource.type="k8s_container"
  resource.labels.cluster_name="production-cluster"
  jsonPayload.level="ERROR"
metricDescriptor:
  metricKind: DELTA
  valueType: INT64
  labels:
    - key: service
      valueType: STRING
labelExtractors:
  service: EXTRACT(jsonPayload.service)

Log-based metrics enable you to track error rates, latency percentiles extracted from log fields, and custom business metrics without modifying application code. Combined with Cloud Monitoring alerts, this creates a powerful pattern for detecting anomalies that don’t surface through infrastructure metrics alone. Log routing rules can also export logs to BigQuery for long-term analysis or to Pub/Sub for real-time processing pipelines.

Cost Management Strategies

GCP’s committed use discounts (CUDs) replace AWS Savings Plans for predictable workloads. The Recommender API surfaces actionable insights based on historical usage patterns:

• Committed Use Recommender analyzes your GKE node usage patterns and suggests 1-year or 3-year commitments with projected savings
• Idle Resource Recommender identifies underutilized nodes and persistent disks that could be downsized or deleted
• Cost Insights in the GKE console shows namespace-level cost attribution, enabling chargeback to teams

Unlike AWS where you manage Reserved Instances separately, GCP CUDs automatically apply to matching resources across your organization. Purchase compute CUDs for GKE nodes, and the discounts flow through without manual assignment. This organization-wide application simplifies financial operations considerably.

The integration between monitoring and billing data enables cost anomaly detection—Cloud Monitoring alerts can trigger when spend exceeds thresholds, combining operational and financial observability in one workflow. Setting up budget alerts with programmatic notifications ensures unexpected cost spikes trigger immediate investigation rather than month-end surprises.

With observability established, connecting your GCP environment to existing AWS infrastructure becomes the next critical step for multi-cloud operations.

Multi-Cloud Networking: Connecting AWS and GCP

Once your workloads run on both clouds, the real challenge begins: making them talk to each other securely and reliably. Multi-cloud networking transforms two isolated environments into a cohesive platform, but the approach you choose depends heavily on your latency requirements, bandwidth needs, and budget.

Dedicated Connectivity: Cloud Interconnect vs Direct Connect

For production workloads requiring consistent, low-latency connectivity, dedicated interconnects provide the foundation. AWS Direct Connect and GCP Cloud Interconnect serve the same purpose but differ in implementation details that matter during planning.

Cloud Interconnect offers two tiers: Dedicated Interconnect for 10 Gbps or 100 Gbps connections, and Partner Interconnect for smaller capacities through service providers. If you already have Direct Connect established with a colocation facility, check whether that same facility offers GCP connectivity—many major colos like Equinix support both clouds, allowing you to extend your existing physical presence rather than building new infrastructure.

The key architectural difference: GCP’s interconnect attaches to a Cloud Router, which handles BGP peering and dynamic route advertisement. This pattern proves more flexible than static routing when managing multiple VPCs across regions.

VPN Tunnels for Development and Testing

For non-production environments or initial migration phases, HA VPN provides a cost-effective alternative. GCP’s HA VPN supports active-active configurations with two tunnels per gateway, delivering 99.99% availability SLA when properly configured.

The setup mirrors what you know from AWS Site-to-Site VPN, with Cloud Router again serving as the BGP endpoint. Expect around 3 Gbps per tunnel—sufficient for development workloads but inadequate for production data replication scenarios.

💡 Pro Tip: When connecting VPN tunnels between clouds, use private ASNs in the 64512-65534 range and document your BGP topology explicitly. Multi-cloud BGP configurations become difficult to troubleshoot without clear ASN allocation records.

Cross-Cloud DNS Resolution

DNS represents the hidden complexity in multi-cloud architectures. Cloud DNS supports forwarding zones that route queries for specific domains to external DNS servers—including your Route 53 private hosted zones. Configure conditional forwarding in both directions: GCP services resolve AWS private zones through DNS forwarding, and Route 53 Resolver endpoints handle the reverse path.

For Kubernetes specifically, ExternalDNS works across both clouds, synchronizing Service and Ingress resources to their respective DNS providers. This approach maintains cloud-native patterns while enabling cross-cluster service discovery.

Service Mesh Considerations

When services span both clouds, a service mesh like Istio provides unified traffic management, security policies, and observability. GKE’s managed Anthos Service Mesh simplifies this considerably, though you’ll need to carefully plan your trust domain configuration to enable mTLS across cloud boundaries.

With networking established, the final piece is developing operational fluency—building the muscle memory that makes GCP feel as natural as your AWS environment.

Building Your GCP Muscle Memory

After six sections of architectural concepts and migration strategies, let’s focus on what matters most for daily productivity: the commands and workflows you’ll use every day. Your AWS CLI muscle memory transfers more directly than you might expect, and with deliberate practice, you’ll achieve the same fluency with gcloud within weeks.

The Rosetta Stone: AWS CLI to gcloud

The command structures parallel each other more than they differ. Both CLIs follow a service-resource-action pattern, though gcloud tends toward longer, more descriptive command names. Here’s your translation guide for the operations you run constantly:

## AWS: Get current identity
aws sts get-caller-identity

## GCP equivalent
gcloud auth list
gcloud config get-value account

## AWS: List EKS clusters
aws eks list-clusters --region us-east-1

## GCP equivalent
gcloud container clusters list --region us-central1

## AWS: Update kubeconfig
aws eks update-kubeconfig --name my-cluster --region us-east-1

## GCP equivalent
gcloud container clusters get-credentials my-cluster --region us-central1

## AWS: Describe cluster
aws eks describe-cluster --name my-cluster --region us-east-1

## GCP equivalent
gcloud container clusters describe my-cluster --region us-central1

One key difference: gcloud uses --project and --region flags consistently across services, whereas AWS varies flag names by service. This consistency actually makes gcloud easier to predict once you internalize the pattern.

Your Multi-Cloud Shell Configuration

Create a unified experience across both clouds. Add these to your .bashrc or .zshrc:

## Quick context switching
alias awsctx='aws sts get-caller-identity && kubectl config current-context'
alias gcpctx='gcloud config get-value project && kubectl config current-context'

## Unified cluster operations
kaws() {
  aws eks update-kubeconfig --name "$1" --region "${2:-us-east-1}"
}

kgcp() {
  gcloud container clusters get-credentials "$1" --region "${2:-us-central1}"
}

## Quick project/account switching
gproj() {
  gcloud config set project "$1"
}

## List all contexts across clouds
alias kcontexts='kubectl config get-contexts -o name | sort'

Cloud Shell: Your GCP Command Center

Cloud Shell deserves special attention as a productivity multiplier. Unlike AWS CloudShell, GCP’s version provides a persistent 5GB home directory, meaning your scripts, dotfiles, and configurations survive between sessions. This makes it ideal as a secure bastion for cluster administration.

Access it at shell.cloud.google.com or click the terminal icon in any GCP Console page. Cloud Shell comes pre-authenticated with your current credentials and includes kubectl, gcloud, terraform, helm, and dozens of other tools pre-installed. For teams with strict security requirements, Cloud Shell eliminates the need to manage credentials on local machines entirely.

Essential Daily Commands

These ten commands cover 80% of your GKE operations:

## Authenticate (run once per session)
gcloud auth login
gcloud auth application-default login

## Set your working project
gcloud config set project my-gcp-project

## Connect to cluster
gcloud container clusters get-credentials production-cluster --region us-central1

## Check node pool status
gcloud container node-pools list --cluster production-cluster --region us-central1

## Resize a node pool
gcloud container clusters resize production-cluster \
  --node-pool default-pool \
  --num-nodes 5 \
  --region us-central1

## View cluster operations (upgrades, scaling)
gcloud container operations list --filter="targetLink:production-cluster"

Context Awareness Script

Keep track of where you are across clouds. Nothing derails your morning like running a production command against the wrong cluster:

## Add to your PS1 or use with starship/powerlevel10k
cloud_context() {
  local k8s_ctx=$(kubectl config current-context 2>/dev/null)
  case "$k8s_ctx" in
    *eks*) echo "☁️ AWS:${k8s_ctx##*/}" ;;
    *gke*) echo "🔷 GCP:${k8s_ctx##*_}" ;;
    *) echo "⚪ ${k8s_ctx:-none}" ;;
  esac
}

The commands become automatic within a week. The concepts from the previous sections—the architectural patterns, networking models, and operational practices—take longer to internalize, but these daily shortcuts accelerate that learning by keeping you productive while you build deeper expertise. Start with the authentication and cluster connection commands, add the aliases as you find yourself repeating patterns, and soon you’ll navigate both clouds with equal confidence.

Key Takeaways

• Start with GCP’s project hierarchy—create a dedicated project for your first GKE cluster and use Workload Identity from day one to avoid service account key sprawl
• Use GKE Autopilot for your initial workloads to eliminate node management overhead while you learn the platform, then graduate to Standard mode when you need GPU nodes or custom configurations
• Translate your EKS Terraform modules incrementally by swapping provider resources while keeping your module structure—don’t rewrite your entire IaC approach
• Set up Cloud VPN between your AWS VPC and GCP VPC immediately to enable gradual migration and hybrid architectures during your transition period