Google Cloud GKE on Azure Tutorial: Architecture, Pricing, Use Cases, and Hands-On Guide for Distributed, hybrid, and multicloud

1. Introduction

GKE on Azure is Google Cloud’s managed Kubernetes offering that lets you create and operate Google Kubernetes Engine (GKE) clusters on Microsoft Azure infrastructure while managing them through Google Cloud.

In simple terms: you run Kubernetes clusters in Azure, but you manage them using Google’s GKE experience (Google Cloud console, Google tooling, and (optionally) GKE Enterprise features), which is useful when your workloads or data must stay in Azure.

Technically, GKE on Azure is part of Google Cloud’s distributed, hybrid, and multicloud portfolio. It provisions and manages Kubernetes clusters composed of Azure resources (compute, networking, load balancers, disks) while integrating those clusters into Google Cloud’s control, policy, and observability planes (for example, fleet management and Cloud Logging/Monitoring integrations where supported and enabled).

The main problem it solves is multicloud Kubernetes operations: providing a consistent Kubernetes platform and operational model across clouds, reducing tool sprawl, improving governance, and enabling standardized security and policy enforcement—without forcing all workloads to move to Google Cloud.

Naming note (important): “GKE on Azure” is the current product name used by Google Cloud for what was previously branded as “Anthos clusters on Azure” / “Anthos on Azure” in older documentation and articles. If you see those older terms, treat them as legacy branding and verify details in current docs.

2. What is GKE on Azure?

Official purpose

GKE on Azure is designed to run and manage GKE clusters on Azure while using Google Cloud as the management plane for cluster lifecycle, policy, and fleet-level operations.

Core capabilities

Provision Kubernetes clusters on Azure from Google Cloud.
Manage cluster lifecycle (create, upgrade, scale, delete) using Google Cloud tooling and APIs.
Attach clusters to a fleet in Google Cloud for centralized governance and (optionally) advanced platform capabilities (for example, policy management and configuration management), depending on your GKE Enterprise licensing and feature support.
Integrate with observability and governance workflows that are common across GKE environments (capabilities vary; verify in official docs).

Major components

While exact implementation details can evolve, conceptually GKE on Azure involves:

Google Cloud project: Hosts the management configuration, APIs, and fleet constructs.
Fleet (GKE Fleet management): A logical grouping to manage multiple clusters consistently (including clusters in Google Cloud, on-prem, and other clouds).
Azure subscription resources: Resource groups, virtual networks/subnets, compute instances, load balancers, managed disks, and other Azure primitives used by the cluster.
Kubernetes clusters on Azure: A control plane and worker nodes running in Azure (as Azure compute resources), managed by Google’s platform.
Identity and access integration: Google Cloud IAM for Google-side management actions and Azure identity/RBAC for Azure-side resource creation (often via an Azure app registration/service principal or equivalent mechanism; verify current requirements).
Connectivity: Secure connectivity between the Google Cloud management plane and Azure-hosted clusters is required (outbound access, firewall rules, DNS, and endpoints must be configured correctly).

Service type

Managed Kubernetes / multicloud managed service under Google Cloud’s Distributed, hybrid, and multicloud category.
It is not the same as Azure Kubernetes Service (AKS). AKS is Microsoft’s managed Kubernetes service; GKE on Azure is Google’s managed Kubernetes distribution deployed onto Azure.

Scope (regional/global/project/subscription)

Management scope: Typically tied to a Google Cloud project (and often a fleet) where APIs, permissions, and cluster registrations live.
Cluster runtime scope: Deployed into a specific Azure region and Azure subscription (and within Azure resource groups and networks).
Availability (regions, features, HA options) can change—verify the supported regions and versions in official docs.

How it fits into the Google Cloud ecosystem

GKE on Azure fits alongside: – GKE (Google Cloud) for clusters running natively on Google Cloud infrastructure. – GKE on-prem / Google Distributed Cloud options for on-prem or edge environments (product names and packaging vary by offering; verify in official docs). – GKE Fleet management to unify operations across these environments. – Optional GKE Enterprise features (licensing-dependent) such as centralized policy and configuration management, and service mesh capabilities—verify exact support for GKE on Azure.

3. Why use GKE on Azure?

Business reasons

Regulatory or contractual requirements: Keep workloads/data in Azure while standardizing Kubernetes operations under a Google Cloud operating model.
Mergers and acquisitions: Integrate platforms when one part of the organization is Azure-heavy and another is already invested in GKE/Google Cloud.
Reduce platform fragmentation: Adopt a consistent Kubernetes baseline, governance model, and operational runbooks across clouds.

Technical reasons

Consistent Kubernetes distribution and tooling: Standardize on GKE APIs and cluster behaviors across environments (within the support matrix).
Centralized policy and config (where enabled): Apply GitOps-style configuration and organization-wide policy controls across multiple clusters.
Portable architecture patterns: Improve workload portability by targeting Kubernetes + standardized ingress/service/observability patterns.

Operational reasons

Unified fleet operations: Standardize cluster inventory, access patterns, and governance across a multicloud estate.
Standardized upgrades and lifecycle management: Use Google Cloud’s cluster lifecycle workflows rather than mixing multiple managed Kubernetes flavors.

Security/compliance reasons

Centralized governance: Fleet-level policy and configuration controls can help enforce baseline security standards across clusters.
Auditability: Centralize audit trails and operational visibility (exact logging/audit details depend on your configuration and enabled integrations—verify).

Scalability/performance reasons

Azure-local scaling: Scale nodes and workloads using Azure compute capacity in the region where you deploy.
Multi-cluster patterns: Build scalable architectures with multiple clusters (for example, per environment, per region, per business unit).

When teams should choose GKE on Azure

Choose GKE on Azure when: – You must run Kubernetes in Azure but want Google Cloud’s approach to Kubernetes management. – You operate multiple Kubernetes environments and need consistent governance and operational tooling. – You plan to use fleet-level capabilities (policy/config/service mesh) across a multicloud estate (licensing and support dependent).

When teams should not choose it

Avoid or reconsider GKE on Azure when: – You want a “native Azure-only” operational model—AKS will often be simpler and more integrated into Azure. – You don’t need centralized multicloud governance and you only run Kubernetes on Azure. – Your organization cannot support the added complexity of two-cloud identity, networking, billing, and operations. – Your workloads depend on specific GKE (Google Cloud) features not available in GKE on Azure—verify feature parity before committing.

4. Where is GKE on Azure used?

Industries

Financial services (data residency + strict governance)
Healthcare and life sciences (compliance-driven platform standardization)
Retail and e-commerce (multi-region availability patterns)
Media and gaming (bursty workloads; multi-environment operations)
Manufacturing and industrial IoT (hybrid/multicloud edge-to-cloud patterns)
Public sector (multi-vendor strategies)

Team types

Platform engineering teams building internal Kubernetes platforms
SRE/operations teams managing clusters at scale
Security engineering teams standardizing policy enforcement
DevOps teams implementing CI/CD and GitOps
Application teams needing standardized deployment targets across environments

Workloads

Microservices platforms
API backends
Batch processing and job runners
Event-driven services (with cloud-specific integrations as needed)
Developer platforms (internal tools, build systems, dev/test clusters)
Legacy application modernization targets (containerized apps)

Architectures

Multicloud active/active or active/passive services (DNS/global traffic management handled outside the cluster; verify your chosen GSLB approach)
Hub-and-spoke governance (central platform team manages fleets; app teams deploy workloads)
Environment-per-cluster (dev/stage/prod)
Tenant isolation per cluster or namespace (depending on security model)

Real-world deployment contexts

Enterprises running strategic workloads on Azure but standardizing Kubernetes under a GKE operating model.
Organizations using Google Cloud for centralized governance while keeping workload execution in Azure regions.

Production vs dev/test usage

Dev/test: Great for validating a multicloud platform pattern and building operational muscle with fleet governance, policy, and GitOps.
Production: Common when there is a clear business requirement to run in Azure while maintaining consistent Kubernetes management. Production readiness depends on supported HA modes, upgrade processes, and network design—verify with official docs and run load tests.

5. Top Use Cases and Scenarios

Below are realistic scenarios where GKE on Azure is a strong fit.

1) Centralized Kubernetes governance for Azure-hosted workloads

Problem: Teams run many Kubernetes clusters in Azure with inconsistent baseline configuration and security controls.
Why GKE on Azure fits: Brings clusters into Google Cloud fleet governance, enabling standardized policy/config patterns (where supported).
Example: A platform team defines organization-wide Kubernetes policies and applies them to all Azure-hosted clusters via fleet tooling.

2) Regulated workload must stay in Azure, but ops team standardizes on GKE

Problem: Compliance mandates Azure residency, but the ops team has deep GKE expertise and existing runbooks.
Why it fits: Runs clusters in Azure while keeping a GKE-aligned management approach.
Example: A payments workload stays in Azure but is operated with the same SRE playbooks used for GKE in Google Cloud.

3) M&A platform consolidation across clouds

Problem: After acquiring a company, one side uses Azure; the other uses Google Cloud and GKE.
Why it fits: Creates a consistent Kubernetes substrate across both clouds.
Example: Standardize cluster lifecycle management, policies, and deployment workflows across inherited Azure infrastructure.

4) Multicloud disaster recovery (DR) for Kubernetes services

Problem: Need DR in a second cloud to reduce dependency on one provider.
Why it fits: You can operate clusters in multiple clouds under a unified fleet model (with careful app/data DR design).
Example: Primary runs in Google Cloud GKE; standby runs in GKE on Azure with periodic data replication (handled by your data layer).

5) Data gravity in Azure + centralized ops in Google Cloud

Problem: Data platforms and integrations are Azure-native; platform governance is centralized in Google Cloud.
Why it fits: Compute runs near Azure data/services; governance stays consistent via Google Cloud.
Example: Services that read from Azure data stores run on GKE on Azure; platform monitoring/policy integrates with Google Cloud where configured.

6) Standardized GitOps across multicloud clusters

Problem: Different clusters use different GitOps tools and conventions.
Why it fits: Fleet-based configuration management patterns can standardize cluster and namespace configs (feature availability depends on your setup).
Example: A single Git repo defines namespaces, RBAC, network policies, and baseline workloads across all clusters.

7) Security posture management across clusters

Problem: Security teams need consistent guardrails and policy enforcement across clouds.
Why it fits: Enables centralized controls and consistent policy workflows (verify exact policy features supported).
Example: Enforce “no privileged pods” and “only approved registries” across all Azure clusters.

8) Blue/green platform upgrades with multiple clusters

Problem: Need safer platform upgrades without impacting all workloads at once.
Why it fits: Supports multi-cluster strategies where you build a new cluster version and gradually migrate workloads.
Example: Stand up a new GKE on Azure cluster on a newer Kubernetes version and shift traffic gradually.

9) Standardized developer experience for multiple environments

Problem: Developers face different ingress, logging, and deployment patterns across clouds.
Why it fits: Encourages consistent cluster add-ons and management practices across environments.
Example: Provide a consistent ingress controller pattern and logging approach across dev and prod clusters.

10) Edge-adjacent workloads executed in Azure regions

Problem: Latency-sensitive apps must run near Azure regions that serve specific geographies.
Why it fits: Runs compute in Azure while using Google Cloud for centralized governance.
Example: Regional API gateways and microservices run on Azure; central policy and inventory are managed from Google Cloud.

6. Core Features

Feature availability can vary by release channel, region, and version. Always cross-check with the official GKE on Azure documentation.

Managed Kubernetes clusters on Azure

What it does: Provisions and manages Kubernetes clusters using Azure infrastructure.
Why it matters: You get a Google-managed Kubernetes distribution while leveraging Azure regions and capacity.
Practical benefit: Standardize operations and Kubernetes APIs across clouds.
Caveats: You pay Azure for infrastructure. You also need to design Azure networking and permissions carefully.

Cluster lifecycle management (create/upgrade/delete)

What it does: Supports controlled cluster operations from Google Cloud tooling.
Why it matters: Reduces manual operations and drift for Kubernetes platform management.
Practical benefit: Repeatable lifecycle workflows; easier to operate fleets of clusters.
Caveats: Upgrades and maintenance windows must be planned around workload SLOs; verify supported upgrade paths and version skew policies.

Node pools (worker capacity management)

What it does: Organizes workers into node pools for scaling and workload placement.
Why it matters: Enables separation of workloads by cost, performance, or security profile.
Practical benefit: Dedicated pools for system workloads, general workloads, and high-memory workloads.
Caveats: Exact autoscaling capabilities and options depend on the current product behavior—verify in docs.

Integration with Google Cloud fleet management

What it does: Lets you organize and manage clusters (including multicloud clusters) under a fleet.
Why it matters: Fleet is the foundation for centralized governance and consistent operations.
Practical benefit: A central inventory of clusters; consistent access patterns and policy (where enabled).
Caveats: Fleet features may require enabling specific APIs and configurations; some features require GKE Enterprise licensing.

Observability integrations (logging/monitoring) where supported

What it does: Provides pathways to integrate cluster telemetry with Google Cloud operations tooling.
Why it matters: Central visibility across clusters reduces MTTR.
Practical benefit: Consistent dashboards and alerting patterns across clusters.
Caveats: Telemetry pipelines, retention, and costs vary. Verify supported integrations and recommended agents/collectors.

Policy and configuration management (GKE Enterprise options)

What it does: Enables GitOps-style configuration synchronization and policy enforcement across clusters (when configured).
Why it matters: Helps enforce security and compliance consistently.
Practical benefit: Version-controlled cluster configuration; automated guardrails.
Caveats: Requires careful repo structure and change control. Feature availability for GKE on Azure should be verified.

Networking and load balancing using Azure primitives

What it does: Exposes Kubernetes services and supports cluster networking via Azure virtual networks and load balancers.
Why it matters: Enables production traffic handling inside Azure.
Practical benefit: Deploy standard Kubernetes Services of type LoadBalancer and integrate with Azure networking.
Caveats: Load balancer costs, IP management, and firewall rules require Azure planning; inbound/outbound rules must also support management connectivity.

Role-based access control (RBAC) and identity integration

What it does: Uses Kubernetes RBAC for in-cluster authorization; uses Google Cloud IAM for Google-side management; uses Azure identity controls for Azure resource management.
Why it matters: Multicloud means multilateral identity boundaries; least privilege is essential.
Practical benefit: Clear separation of duties between platform operators and application teams.
Caveats: Misconfigured service principals / credentials and overly broad roles are common sources of risk.

7. Architecture and How It Works

High-level architecture

GKE on Azure is best understood as two planes:

Management plane (Google Cloud)
– Google Cloud project contains the configuration, APIs, and (often) fleet membership records. – Operators use Google Cloud console/CLI to manage clusters. – Governance tools (policy/config management) integrate at fleet level if enabled.
Runtime plane (Azure)
– Clusters run on Azure resources (compute, networking, storage). – Kubernetes API endpoints and node networking live in Azure. – Workloads consume Azure-local services (databases, messaging, identity, etc.) as needed.

Control flow and data flow

Control flow: Admin/operator actions (create cluster, upgrade, scale) originate from Google Cloud tooling and are applied to Azure resources through configured credentials and management components.
Data flow: Application traffic remains within Azure networking unless you route it elsewhere. Observability data and management signals may flow to Google Cloud services depending on your configuration.

Integrations with related services

Common integrations in multicloud designs include: – Fleet management (Google Cloud): group clusters and apply consistent governance. – Cloud Logging / Cloud Monitoring (Google Cloud): central telemetry (where enabled). – Secret Manager (Google Cloud) or Azure Key Vault: secret storage choices; patterns vary (you must choose a supported, secure approach). – CI/CD: Cloud Build, GitHub Actions, Azure DevOps, or other pipelines that deploy to the cluster using Kubernetes credentials.

Dependency services

At minimum: – A Google Cloud project with required APIs enabled. – An Azure subscription with networking and IAM prepared. – Network connectivity that allows required control/telemetry communications.

Security/authentication model (conceptual)

Google Cloud IAM controls who can create/manage clusters and fleet resources in the Google Cloud project.
Azure IAM/RBAC controls which Azure resources can be created/managed by the GKE on Azure provisioning process (often through a dedicated Azure identity).
Kubernetes RBAC controls access inside the cluster.
Network security (Azure NSGs, firewalls, routing) controls traffic between nodes, between clusters, and to external endpoints.

Networking model (conceptual)

Clusters are deployed into an Azure VNet and subnets.
Nodes and pods use Kubernetes networking; you must plan CIDRs to avoid overlap with on-prem or other clouds.
Inbound traffic commonly enters via an Azure load balancer created for Kubernetes Service resources.
Outbound connectivity must support:
Access to container registries (Artifact Registry, Azure Container Registry, or others).
Access to Google Cloud endpoints needed for management/telemetry (if enabled).
Access to Azure APIs for infrastructure operations.

Monitoring/logging/governance considerations

Decide early where telemetry should live (Google Cloud, Azure, or both).
For regulated environments, ensure logs are retained and access-controlled appropriately.
Standardize labels/tags and cluster naming so cost allocation and inventory queries work across clouds.

Simple architecture diagram

flowchart LR
  Dev[Operator / CI-CD] --> GC[Google Cloud project]
  GC --> Fleet[Fleet management]
  GC --> API[GKE on Azure APIs]
  API --> AzureSub[Azure subscription]
  AzureSub --> Cluster[GKE on Azure cluster]
  Cluster --> Apps[Workloads]
  Apps --> Users[End users]

Production-style architecture diagram

flowchart TB
  subgraph GoogleCloud["Google Cloud (Management Plane)"]
    GCProj["Google Cloud Project"]
    Fleet["Fleet (cluster registry & governance)"]
    Policy["Policy/Config Mgmt (optional, licensing-dependent)"]
    Obs["Cloud Logging/Monitoring (optional)"]
    IAMG["Google Cloud IAM"]
  end

  subgraph Azure["Microsoft Azure (Runtime Plane)"]
    Sub["Azure Subscription"]
    RG["Resource Group(s)"]
    VNet["VNet / Subnets"]
    LB["Azure Load Balancer"]
    CP["Kubernetes Control Plane (Azure compute)"]
    NP1["Node Pool A"]
    NP2["Node Pool B"]
    Disks["Managed Disks / Storage"]
    NSG["NSG / Firewall rules"]
  end

  Users["Users / Clients"] --> DNS["DNS / Traffic Manager (your choice)"] --> LB --> Apps["Kubernetes Services/Ingress"] --> NP1
  Apps --> NP2
  NP1 --> Disks
  NP2 --> Disks

  DevOps["CI/CD (GitHub Actions / Azure DevOps / Cloud Build)"] --> CP

  IAMG --> GCProj
  GCProj --> Fleet
  Fleet --> CP
  Policy --> CP
  Obs --> CP

  Sub --> RG --> VNet --> CP
  NSG --> VNet

8. Prerequisites

Because GKE on Azure spans two clouds, prerequisites are broader than single-cloud Kubernetes.

Accounts, projects, and subscriptions

Google Cloud:
A Google Cloud account.
A Google Cloud project dedicated to platform management is recommended.
Billing enabled on the project.
Azure:
An Azure subscription where clusters will be deployed.
Ability to create resource groups, VNets/subnets, compute, load balancers, and identity objects.

Permissions / IAM roles

Google Cloud IAM: You need permissions to:
Enable APIs
Create/manage GKE on Azure resources
Manage fleet membership (if using fleet)
Create service accounts and manage IAM bindings (common for automation)
Exact roles can change; verify in official docs.
Azure RBAC: You need permissions to:
Create or provide a VNet/subnet
Create resource groups/resources
Create and manage an identity used for provisioning (often a service principal/app registration)
Configure role assignments at the right scope (subscription or resource group)

Billing requirements

Google Cloud billing for management features and any enabled Google Cloud services (pricing depends on licensing/SKUs).
Azure billing for all Azure resources created (VMs, disks, load balancers, public IPs, egress, etc.).

Tools and CLIs

Google Cloud SDK (gcloud): https://cloud.google.com/sdk/docs/install
Azure CLI (az): https://learn.microsoft.com/cli/azure/install-azure-cli
kubectl: https://kubernetes.io/docs/tasks/tools/
Optional but common:
Terraform (if you automate Azure networking/identity): https://developer.hashicorp.com/terraform/downloads

Region availability

You must pick:
A Google Cloud location for the management configuration (varies by service design).
An Azure region for cluster runtime.
Availability and supported regions can change—verify in official docs:
https://cloud.google.com/kubernetes-engine/multi-cloud/docs/azure (entry point)

Quotas / limits

Azure: vCPU quotas, public IP quotas, load balancer limits, and regional constraints.
Google Cloud: API quotas and project-level quotas.
Always check quotas before provisioning, especially in new Azure subscriptions.

Prerequisite services

Required Google Cloud APIs for GKE on Azure and fleet management (exact API names can change; enable them through the console “APIs & Services” page or follow the current setup guide).
Azure resource providers registered (typically handled automatically in many subscriptions; verify if failures occur).

9. Pricing / Cost

Pricing for GKE on Azure is inherently multidimensional because you pay for: 1) Google Cloud-side management / licensing, and
2) Azure-side infrastructure, plus
3) Operational overhead costs (observability, data egress, etc.)

Official pricing sources (start here)

Google Cloud pricing for Anthos / GKE Enterprise (packaging and SKUs can change):
https://cloud.google.com/anthos/pricing
Google Cloud Pricing Calculator:
https://cloud.google.com/products/calculator
Azure pricing pages for compute/network/storage relevant to your chosen VM sizes and region:
https://azure.microsoft.com/pricing/

If your organization purchases GKE Enterprise via a contract, your effective price may be negotiated and not publicly listed.

Pricing dimensions (what you are billed for)

Google Cloud side

Common cost dimensions in Google Cloud multicloud Kubernetes offerings include: – GKE Enterprise / Anthos licensing (often tied to vCPU usage or a subscription model; verify current SKUs). – Fleet management features (may be included in GKE Enterprise packaging; verify). – Optional Google Cloud services you enable for the clusters: – Cloud Logging ingestion and retention – Cloud Monitoring metrics – Artifact Registry storage and egress – Secret Manager operations – Cloud NAT / networking (if you route traffic via Google Cloud—less common for GKE on Azure runtime)

Azure side

You will pay Azure for: – Compute: control plane and worker nodes (VMs) – Storage: managed disks for nodes and persistent volumes – Networking: – Load balancers – Public IP addresses (if used) – Bandwidth/egress (especially cross-cloud traffic) – NAT gateways (if applicable) – Azure-native services your workloads use (databases, queues, storage accounts, etc.)

Cost drivers (what makes bills go up)

Number and size of nodes (and whether control plane runs on dedicated VMs—commonly the case in non-AKS Kubernetes; verify exact architecture)
High availability: multi-zone/multi-replica designs increase VM and load balancer costs
Observability volume: logs and metrics can be a major cost if not filtered and sampled
Cross-cloud egress: moving data between Azure and Google Cloud usually incurs egress charges on at least one side
Persistent storage: disk size, IOPS tiers, snapshots/backups
Load balancer count: each Service of type LoadBalancer can create billable Azure LBs and public IPs (depending on configuration)

Hidden or indirect costs to plan for

Two-cloud operations overhead: identity, networking, and incident response across Google Cloud + Azure.
IP address management: planning CIDR ranges and avoiding overlap may require extra network engineering.
Security tooling: scanning, policy, key management, and audits across environments.
Training: teams need familiarity with both Azure primitives and Google Cloud management constructs.

Network / data transfer implications

Keep high-volume app traffic within Azure when possible to avoid cross-cloud egress.
If you centralize logs in Google Cloud but workloads are in Azure, you may pay for:
Telemetry export bandwidth (Azure egress)
Google Cloud logging ingestion and retention

How to optimize cost

Right-size node pools: choose VM sizes and scaling policies that match workload profiles.
Use fewer load balancers: prefer Ingress/Gateway patterns where appropriate rather than many LoadBalancer services.
Control observability volume:
Reduce noisy logs at source
Set retention appropriately
Use metrics sampling and limit high-cardinality labels
Avoid cross-cloud chatter: co-locate dependencies with workloads (in Azure) unless there’s a strong reason not to.
Separate environments: dev/test clusters can be small and scheduled to shut down where feasible (verify whether your operational model supports this cleanly).

Example low-cost starter estimate (conceptual)

A low-cost evaluation typically includes: – One small cluster in a single Azure region – Minimal node count (one small node pool) – Minimal load balancers (one) – Limited logging retention and filtered logs

Exact pricing varies by: – Azure region and VM SKU pricing – Disk types – GKE Enterprise licensing model and any contract discounts

Use the official calculators and treat any third-party blog numbers as unreliable.

Example production cost considerations

In production, expect: – Multiple clusters (prod + staging + dev) and/or multiple regions – HA requirements: multiple control plane/worker instances – More node pools for separation of workloads – More strict monitoring/alerting (more metrics) – Centralized logging retention and possibly SIEM export – Backup and disaster recovery costs – Dedicated network connectivity and egress budgeting

10. Step-by-Step Hands-On Tutorial

This lab focuses on a realistic but safe beginner workflow: prepare Azure prerequisites, create a small GKE on Azure cluster using the Google Cloud console (so you always use the latest supported fields), connect with kubectl, deploy a sample app, verify it, and clean up.

Objective

Prepare an Azure subscription for GKE on Azure provisioning.
Create a minimal GKE on Azure cluster from Google Cloud.
Connect to the cluster using kubectl.
Deploy a simple web app and expose it.
Validate basic operations and then delete resources to avoid ongoing cost.

Lab Overview

You will: 1. Create (or choose) a Google Cloud project and enable the relevant APIs. 2. In Azure: create a resource group and network, and create an identity (service principal) that GKE on Azure can use to create Azure resources (if required by the current setup flow). 3. In Google Cloud console: create a GKE on Azure cluster using the official UI wizard. 4. Get cluster credentials and deploy a sample app. 5. Validate and clean up.

Important: Setup requirements change over time (especially for identity and networking). For any step where the exact fields differ in your environment, follow the current official guide for “Create a GKE on Azure cluster” and use this lab as the operational walkthrough. Official doc entry point: https://cloud.google.com/kubernetes-engine/multi-cloud/docs/azure

Step 1: Create/select a Google Cloud project and enable billing

In the Google Cloud console, create or select a project: – https://console.cloud.google.com/projectselector2/home/dashboard
Ensure billing is enabled for the project: – https://console.cloud.google.com/billing

Expected outcome: You have a Google Cloud project with billing enabled.

Verification: – In the console project dashboard, confirm the correct project is selected. – In Billing, confirm the project is linked to a billing account.

Step 2: Install required CLIs locally

Install the tools on your workstation (or Cloud Shell, where applicable).

Google Cloud SDK: https://cloud.google.com/sdk/docs/install
Azure CLI: https://learn.microsoft.com/cli/azure/install-azure-cli
kubectl: https://kubernetes.io/docs/tasks/tools/

Expected outcome: gcloud, az, and kubectl are available.

Verification:

gcloud version
az version
kubectl version --client

Step 3: Enable required Google Cloud APIs (console-driven, safest)

Because API names and required combinations can change, enable APIs from the console: 1. Go to APIs & Services → Library:
https://console.cloud.google.com/apis/library 2. Search for and enable: – GKE Multi-Cloud API (or the current API used for GKE on Azure) – Kubernetes Engine API (often useful in related tooling) – GKE Hub / Fleet related APIs if you plan to use fleet management features

Expected outcome: Required APIs show “Enabled” in your project.

Verification: – APIs & Services → Enabled APIs:
https://console.cloud.google.com/apis/dashboard

Step 4: Prepare Azure: login and select subscription

On your workstation:

az login
az account list --output table
az account set --subscription "<YOUR_AZURE_SUBSCRIPTION_ID>"

Expected outcome: Azure CLI is authenticated and targeting the subscription you will use.

Verification:

az account show --output table

Step 5: Create an Azure resource group and network (baseline)

Pick an Azure region supported by your organization and by GKE on Azure (verify support in official docs).

export AZ_LOCATION="eastus"              # example; choose your region
export AZ_RG="rg-gke-on-azure-lab"
export AZ_VNET="vnet-gke-on-azure-lab"
export AZ_SUBNET="subnet-gke-on-azure"

az group create \
  --name "${AZ_RG}" \
  --location "${AZ_LOCATION}"

az network vnet create \
  --resource-group "${AZ_RG}" \
  --name "${AZ_VNET}" \
  --location "${AZ_LOCATION}" \
  --address-prefixes "10.10.0.0/16" \
  --subnet-name "${AZ_SUBNET}" \
  --subnet-prefixes "10.10.1.0/24"

Expected outcome: You have an Azure resource group, VNet, and subnet.

Verification:

az group show --name "${AZ_RG}" --output table
az network vnet show --resource-group "${AZ_RG}" --name "${AZ_VNET}" --output table
az network vnet subnet show --resource-group "${AZ_RG}" --vnet-name "${AZ_VNET}" --name "${AZ_SUBNET}" --output table

CIDR planning note: In real environments, coordinate IP ranges with your network team to avoid overlap with other VNets, on-prem networks, and other clusters.

Step 6: Create an Azure identity for provisioning (service principal) if required

Many multicloud provisioning workflows require an Azure identity that can create/modify resources in your resource group. The exact permissions required are defined in the official setup guide—do not over-permission.

Create a service principal scoped to the resource group:

export AZ_SP_NAME="sp-gke-on-azure-lab"

AZ_SCOPE=$(az group show --name "${AZ_RG}" --query id -o tsv)

az ad sp create-for-rbac \
  --name "${AZ_SP_NAME}" \
  --role "Contributor" \
  --scopes "${AZ_SCOPE}" \
  --output json

Expected outcome: You receive JSON output containing: – appId (client ID) – password (client secret) – tenant

Verification: – Save the output securely. Do not commit it to Git. – Confirm the role assignment exists:

export AZ_APP_ID="<APP_ID_FROM_OUTPUT>"
az role assignment list --assignee "${AZ_APP_ID}" --scope "${AZ_SCOPE}" --output table

Security note: “Contributor” is commonly used for labs, but production should follow least privilege per the official permission list. Reduce scope (resource-group vs subscription) whenever possible.

Step 7: Create a GKE on Azure cluster in Google Cloud console

Console steps are recommended here because the UI wizard stays aligned with the current supported parameters.

In Google Cloud console, go to Kubernetes Engine: – https://console.cloud.google.com/kubernetes
Find the multicloud section and choose GKE on Azure (naming and navigation can change).
Click Create and follow the wizard. You will typically provide: – Cluster name – Azure subscription ID – Azure tenant ID – Azure client ID and client secret (if the workflow uses a service principal) – Azure resource group, VNet, subnet – Azure region – Node pool size and count – Optional: logging/monitoring integration choices

Use the official guide side-by-side to ensure you provide the current required fields: – https://cloud.google.com/kubernetes-engine/multi-cloud/docs/azure (navigate to “Create a cluster”)

Expected outcome: A new GKE on Azure cluster is created and becomes “Ready” (or equivalent status).

Verification: – In the cluster details page, verify: – Cluster status is healthy/ready – Node pools are created – The cluster has a connect/get-credentials option available

Timeline note: Cluster creation can take several minutes. If it fails, jump to the Troubleshooting section and check identity/network/quota issues.

Step 8: Get kubeconfig credentials and connect with kubectl

In the Google Cloud console cluster page, use the Connect button and copy the exact command provided (this avoids CLI syntax drift).

It typically resembles:

gcloud container azure clusters get-credentials <CLUSTER_NAME> --location <LOCATION>

Run the command shown by your console.

Expected outcome: Your local kubeconfig is updated with a new context for the cluster.

Verification:

kubectl config get-contexts
kubectl get nodes
kubectl get ns

You should see nodes in Ready state.

Step 9: Deploy a sample app and expose it

Deploy a small web server and expose it via a LoadBalancer.

kubectl create namespace web
kubectl -n web create deployment hello \
  --image=nginx:stable

kubectl -n web expose deployment hello \
  --port 80 \
  --type LoadBalancer

Expected outcome: – A Deployment is created. – A Service of type LoadBalancer is created, and Azure provisions a load balancer (may take a few minutes).

Verification:

kubectl -n web get deploy,svc,pods -o wide
kubectl -n web get svc hello -w

Wait until EXTERNAL-IP (or equivalent) is assigned.

Then test:

export EXTERNAL_IP="<VALUE_FROM_kubectl_get_svc>"
curl -I "http://${EXTERNAL_IP}/"

You should receive an HTTP 200 response header from NGINX.

Step 10: (Optional) Basic observability checks

Depending on what you enabled during cluster creation, check: – Google Cloud console logs/metrics (if integrated) – Azure metrics (load balancer, VM metrics)

If you enabled Google Cloud Logging, navigate to Logs Explorer: – https://console.cloud.google.com/logs/query

Expected outcome: You can find Kubernetes-related logs (exact log names depend on your telemetry configuration).

Verification: – Filter for Kubernetes container logs (query patterns vary; use the UI’s resource filters).

Validation

Use this quick checklist:

Cluster is Ready/Healthy in Google Cloud console.
kubectl get nodes shows Ready nodes.
Sample app Pod is Running: bash kubectl -n web get pods
Service has an external IP and responds to HTTP: bash kubectl -n web get svc hello curl -I http://<external-ip>/

Troubleshooting

Common issues and practical fixes:

1) Cluster creation fails with permissions/authorization errors

Likely cause: Azure service principal lacks required permissions at the correct scope.
Fix:
Re-check role assignment scope (resource group vs subscription).
Confirm correct tenant/subscription IDs were entered.
Verify the client secret hasn’t expired.
Follow the exact permission requirements in official docs.

2) Quota errors in Azure

Likely cause: Not enough vCPU quota in the chosen region or SKU family.
Fix:
Check quotas in Azure portal.
Request quota increase or select a smaller VM size.

3) `kubectl get nodes` hangs or cannot reach cluster

Likely cause: You didn’t run the exact connect/get-credentials command for the right cluster/location.
Fix:
Re-run the command copied from the console “Connect” button.
Confirm your kubeconfig context.

4) LoadBalancer service never gets an external IP

Likely cause: Azure networking restrictions, missing permissions, or limits on public IPs/LB resources.
Fix:
Describe the service: bash kubectl -n web describe svc hello
Check events for errors about cloud provider integration.
Verify Azure quotas and that the provisioning identity can create LB and public IP resources.

5) Costs higher than expected during lab

Likely cause: Large VM sizes, multiple load balancers, or leaving resources running.
Fix:
Proceed to Cleanup immediately after validation.
Confirm all clusters and Azure resources are deleted.

Cleanup

To avoid ongoing charges, remove both Kubernetes objects and the underlying cluster and Azure resources.

1) Delete the sample workload

kubectl delete namespace web

2) Delete the GKE on Azure cluster

Use the Google Cloud console to delete the cluster (recommended to ensure all managed resources are cleaned up according to the current workflow). Alternatively, if your environment provides a CLI delete command in the cluster page, you can use it.

Verification: – Cluster no longer appears in the Google Cloud console. – Azure resource group no longer contains cluster resources (some resources may remain depending on the chosen deletion options—verify).

3) Delete Azure resources created for the lab

If you used a dedicated resource group:

az group delete --name "${AZ_RG}" --yes --no-wait

Optionally, delete the service principal (be careful—only if it was created for this lab):

az ad sp delete --id "${AZ_APP_ID}"

Final verification: – Azure portal shows the resource group deleted. – Google Cloud console shows no remaining GKE on Azure clusters in the project.

11. Best Practices

Architecture best practices

Design for failure domains: Model how Azure region/zone failures affect control plane, nodes, and load balancers. Use HA options where required and supported.
Separate environments: Use separate clusters for dev/staging/prod. Use separate Azure subscriptions or resource groups and separate Google Cloud projects if governance demands it.
Plan IP ranges early: Avoid overlapping CIDRs across VNets, on-prem, and other clusters. Document allocations.
Minimize cross-cloud dependencies: Keep latency-sensitive and high-throughput dependencies in the same cloud/region as the workloads.

IAM/security best practices

Least privilege for Azure provisioning identity:
Scope permissions to the smallest viable scope (often resource group).
Rotate secrets and consider managed identity approaches if supported (verify).
Separate duties:
Platform admins manage clusters and fleet configuration.
App teams deploy into namespaces with limited RBAC.
Use Kubernetes RBAC + namespaces:
Separate workloads by team/tenant.
Limit cluster-admin access.

Cost best practices

Right-size node pools and use autoscaling where supported and appropriate.
Avoid LB sprawl: prefer an ingress strategy to consolidate exposure.
Tune telemetry: reduce noisy logs and high-cardinality labels; set retention intentionally.
Tag and label everything:
Azure tags on resource groups/resources for cost allocation
Google Cloud labels on cluster resources (where applicable)
Kubernetes labels/annotations for internal chargeback

Performance best practices

Use multiple node pools for different workload shapes (CPU vs memory vs system).
Resource requests/limits: enforce sane defaults and limit ranges.
Pod disruption budgets for critical services.
Load testing: validate autoscaling behavior and LB performance before production rollout.

Reliability best practices

Backup and restore:
Back up Kubernetes manifests (GitOps) and data (volume snapshots + application-level backups).
Regularly test restore procedures.
Upgrade discipline:
Use maintenance windows.
Canary upgrades: upgrade a non-prod cluster first, then a small prod cluster, then the rest.
Multi-cluster traffic strategy:
Use DNS-based failover or a global traffic manager (external to Kubernetes) appropriate for your org.

Operations best practices

Standardize runbooks: incident response, node pool scaling, certificate rotation, and upgrade procedures.
Alerting: alert on SLO symptoms (latency, error rate) and platform signals (node not ready, pod crash loops).
Central inventory: keep an authoritative list of clusters, owners, environments, and purpose.

Governance/tagging/naming best practices

Adopt a consistent naming scheme:
Cluster: env-region-platform (example: prod-eastus-payments)
Node pool: system, general, batch, gpu (if applicable)
Maintain an ownership registry (team, cost center, escalation contact).
Use policy to prevent unmanaged clusters and ad-hoc configuration drift.

12. Security Considerations

Identity and access model

You must secure three layers: 1. Google Cloud IAM: who can create/manage GKE on Azure clusters and fleet objects. 2. Azure RBAC: what Azure resources can be created/changed by the provisioning identity and operators. 3. Kubernetes RBAC: what users and service accounts can do inside the cluster.

Recommendations: – Use dedicated Google Cloud projects for platform management. – Use dedicated Azure subscriptions/resource groups for clusters. – Restrict who can create clusters (platform team only). – Use short-lived credentials where possible; rotate secrets.

Encryption

In transit: Use TLS for Kubernetes API access and for application ingress.
At rest:
Azure disks and managed storage provide encryption-at-rest options (verify defaults and required settings in Azure).
Kubernetes secrets are base64-encoded by default; use a secrets management strategy:
- Kubernetes encryption at rest (if configured and supported)
- External secret manager patterns (Google Secret Manager or Azure Key Vault) depending on your standards and supported integrations (verify)

Network exposure

Minimize public endpoints:
Prefer private networking and controlled ingress where possible.
Use Network Security Groups (NSGs) and firewall rules:
Restrict node subnet inbound access.
Restrict management ports and API endpoints.
Ensure required outbound traffic for management/telemetry is allowed, but avoid broad “allow all egress” in production unless justified.

Secrets handling

Do not store Azure client secrets in source control.
Use a secure secret store and rotate credentials.
Prefer workload identity patterns for applications when supported, rather than long-lived cloud keys (support varies by environment—verify).

Audit/logging

Enable and retain:
Kubernetes audit logs (where supported)
Google Cloud audit logs for management actions
Azure activity logs for resource modifications
Centralize logs into your SIEM with clear retention and access controls.

Compliance considerations

Define data residency:
Workloads and their data remain in Azure by design, but telemetry and management metadata may flow to Google Cloud depending on configuration.
For regulated environments:
Review what metadata is stored in Google Cloud.
Align retention, access, and encryption policies with your compliance requirements.

Common security mistakes

Over-permissioning the Azure service principal at subscription scope.
Exposing Kubernetes API endpoints publicly without strict controls.
Allowing cluster-admin to too many users.
Shipping all logs without filtering (risk + cost).
Overlooking cross-cloud egress paths that bypass inspection controls.

Secure deployment recommendations

Establish a baseline cluster security profile:
RBAC, Pod Security standards, network policies (where applicable), image provenance/scanning, and admission policies.
Enforce policies via a centralized mechanism (fleet policy tooling where supported).
Implement regular vulnerability scanning and patching processes.

13. Limitations and Gotchas

Because this is a multicloud service, expect additional constraints compared to single-cloud Kubernetes.

Known limitation patterns (verify specifics)

Feature parity: Not all GKE (Google Cloud) features are necessarily available in GKE on Azure. Always consult the current support matrix.
Region support: Limited to supported Azure regions and versions—verify before designing production.
Networking constraints: VNet/subnet design, CIDR overlap, and firewall rules are frequent sources of deployment issues.
Identity complexity: Two-cloud IAM and credential rotation add operational overhead.

Quotas

Azure quotas (vCPU, public IP, load balancers) can block cluster creation or scaling.
Google Cloud API quotas can limit automation at scale.

Regional constraints

Some Azure services or VM SKUs may not be available in all regions.
Support for zones/HA can vary by region—verify.

Pricing surprises

Multiple LoadBalancer services can multiply Azure LB and public IP costs.
High log volume exported to Google Cloud can be expensive.
Cross-cloud data transfer costs can dominate if workloads frequently communicate with services in the other cloud.

Compatibility issues

Container image architecture mismatches (ensure node architecture matches images).
Ingress/controller differences across environments.
Monitoring/logging agent differences compared to native GKE.

Operational gotchas

Incident response requires access to both Google Cloud and Azure portals/logs.
Misalignment between cluster deletion and Azure resource cleanup can leave billable resources behind—always verify Azure resource groups after deletions.

Migration challenges

Moving from AKS (or self-managed Kubernetes) to GKE on Azure requires:
Revalidating ingress, storage classes, network policies, and identity integrations
Testing CI/CD assumptions
Adjusting observability pipelines

Vendor-specific nuances

GKE on Azure is “Google-managed Kubernetes on Azure infrastructure.” Some teams assume it behaves like AKS; it does not. Treat it as its own platform with its own lifecycle, support model, and design constraints.

14. Comparison with Alternatives

GKE on Azure is one option in a broader Kubernetes platform landscape.

Option	Best For	Strengths	Weaknesses	When to Choose
GKE on Azure (Google Cloud)	Multicloud governance while running in Azure	Consistent GKE-style management; fleet-based governance options; aligns with Google Cloud platform operations	Two-cloud complexity; may not match AKS integrations; feature parity must be verified	You need Azure runtime with Google Cloud governance/standardization
GKE (Google Cloud)	Kubernetes on Google Cloud	Deep integration with Google Cloud services; mature managed experience	Doesn’t satisfy “must run in Azure” requirements	Workloads can run in Google Cloud and you want best GKE integration
AKS (Azure Kubernetes Service)	Kubernetes on Azure with Azure-native ops	Tight Azure integration; simpler single-cloud model; broad Azure ecosystem compatibility	Less alignment with GKE fleet governance model; different APIs/add-ons	You are primarily Azure-centric and don’t need Google Cloud governance
Self-managed Kubernetes on Azure (kubeadm, etc.)	Maximum control/customization	Full control over configuration and components	High ops burden; upgrades and security are on you	Specialized requirements not met by managed offerings
Other multicloud platforms (open-source + GitOps)	Toolchain-based standardization	Cloud-agnostic patterns; avoid vendor lock-in	You still manage the platform details; integration effort is high	You prefer a DIY platform approach and have strong platform engineering capacity

15. Real-World Example

Enterprise example: regulated insurer with Azure data residency

Problem: An insurer must keep customer PII and claims processing data in Azure for residency and existing enterprise agreements. Meanwhile, the central platform team standardizes Kubernetes governance using Google Cloud fleet-based practices and wants consistent security guardrails.
Proposed architecture:
Run GKE on Azure clusters in the required Azure regions.
Attach clusters to a Google Cloud fleet for centralized inventory and governance.
Use GitOps-style configuration management for baseline cluster configuration (namespaces, RBAC, network policies, ingress conventions), if supported and licensed.
Export platform logs to the organization’s SIEM; retain Azure activity logs and Google Cloud audit logs.
Why GKE on Azure was chosen:
Meets Azure residency needs.
Allows the platform team to operate with a consistent GKE-aligned approach across environments.
Expected outcomes:
Reduced configuration drift across clusters.
Improved audit readiness via centralized policy baselines and clearer operational controls.
More consistent incident response playbooks across clouds.

Startup/small-team example: SaaS with Azure-first customers and Google Cloud platform expertise

Problem: A SaaS startup has large customers with Azure networking peering and prefers deploying customer-facing services in Azure for latency and enterprise connectivity. The engineering team has prior GKE experience and wants to keep tooling consistent.
Proposed architecture:
Run one production GKE on Azure cluster per region (or per environment) depending on scale.
Use a single ingress strategy and standard Helm/Kustomize deployment pipelines.
Keep dependencies (databases, queues) in Azure to reduce cross-cloud egress.
Why GKE on Azure was chosen:
Azure runtime fits customer network expectations.
The team leverages existing GKE operational knowledge.
Expected outcomes:
Faster time-to-production on Azure without rebuilding the entire platform approach.
More consistent deployment patterns across environments as the startup grows.

16. FAQ

Is GKE on Azure the same as AKS?
No. AKS is Microsoft’s managed Kubernetes service. GKE on Azure is Google’s managed GKE distribution running on Azure infrastructure.
Do I manage Azure VMs myself?
You pay for Azure resources and must plan quotas/networking/identity. Day-to-day node and cluster lifecycle operations are managed through GKE on Azure workflows, but responsibilities are shared. Verify the responsibility split in official docs.
Do I need both Google Cloud and Azure accounts?
Yes. You need a Google Cloud project for management and an Azure subscription for the runtime resources.
Where do my application data and traffic live?
Application traffic typically stays in Azure unless you route it elsewhere. Management metadata and optional telemetry may be sent to Google Cloud depending on configuration.
What is a fleet and why does it matter?
A fleet is a Google Cloud concept for grouping clusters for centralized management and governance. Many multicloud governance features build on fleet membership.
Can I use GitOps with GKE on Azure?
Often yes via fleet-based configuration management options, depending on your licensing and supported features. Verify GKE on Azure compatibility in official docs.
How do upgrades work?
Upgrades are performed through Google Cloud’s GKE on Azure lifecycle tooling. Always validate upgrade paths, maintenance windows, and version skew in official docs.
Can I deploy stateful workloads?
Yes, Kubernetes supports stateful workloads, but you must design storage classes, backups, and availability appropriately using Azure storage primitives and your backup tooling.
How does load balancing work?
Kubernetes Service of type LoadBalancer typically provisions an Azure Load Balancer. Ingress patterns depend on your chosen controller and supported integrations.
Is there a free tier?
Any free tier depends on current Google Cloud packaging and trial options, and it won’t cover Azure infrastructure costs. Check the official pricing pages.
How do I estimate cost accurately?
Combine: – Google Cloud GKE Enterprise/Anthos pricing model (verify current SKUs) – Azure VM/storage/network pricing for your selected region/SKUs – Telemetry volume estimates and retention
Can I use Azure Container Registry (ACR) with GKE on Azure?
Typically yes since the cluster runs in Azure, but you must configure authentication and network access. Verify recommended patterns.
Can I use Artifact Registry from Google Cloud?
You can, but pulling images across clouds may incur egress and add latency. Many teams keep registries in the same cloud as the runtime.
How is access controlled for developers?
Commonly via Kubernetes RBAC and namespaces, plus your organization’s identity integration patterns. Keep platform-admin privileges restricted.
What’s the biggest operational risk?
Underestimating two-cloud complexity: identity, networking, quotas, and troubleshooting require mature platform practices and clear ownership.

17. Top Online Resources to Learn GKE on Azure

Resource Type	Name	Why It Is Useful
Official documentation	GKE on Azure documentation (entry point) – https://cloud.google.com/kubernetes-engine/multi-cloud/docs/azure	The authoritative, current setup and operations guide
Official pricing	Anthos / GKE Enterprise pricing – https://cloud.google.com/anthos/pricing	Explains Google Cloud-side pricing model (verify latest SKUs)
Official calculator	Google Cloud Pricing Calculator – https://cloud.google.com/products/calculator	Build scenario-based estimates for Google Cloud costs
Azure pricing	Azure Pricing – https://azure.microsoft.com/pricing/	Estimate Azure compute/network/storage costs that dominate runtime spend
Fleet management	Fleet management docs – https://cloud.google.com/anthos/fleet-management/docs	Learn cluster grouping, governance patterns, and fleet capabilities
Config management	Anthos Config Management / Config Sync – https://cloud.google.com/anthos-config-management/docs	GitOps-style configuration patterns and guardrails (verify support for your cluster type)
Policy	Policy Controller – https://cloud.google.com/anthos-config-management/docs/concepts/policy-controller	Policy enforcement concepts and workflows (verify support)
Google Cloud SDK	gcloud SDK docs – https://cloud.google.com/sdk/docs	CLI installation and authentication
Azure CLI	Azure CLI docs – https://learn.microsoft.com/cli/azure/	Needed for Azure-side setup
Kubernetes basics	Kubernetes docs – https://kubernetes.io/docs/home/	Core Kubernetes concepts used in any cluster
Architecture guidance	Google Cloud Architecture Center – https://cloud.google.com/architecture	Reference architectures and patterns (filter for hybrid/multicloud topics)
Video learning	Google Cloud Tech YouTube – https://www.youtube.com/@googlecloudtech	Product walkthroughs and conceptual sessions (search for “GKE multicloud” / “GKE on Azure”)

18. Training and Certification Providers

Institute	Suitable Audience	Likely Learning Focus	Mode	Website URL
DevOpsSchool.com	DevOps engineers, SREs, platform teams, architects	DevOps, Kubernetes, cloud operations, CI/CD	Check website	https://www.devopsschool.com/
ScmGalaxy.com	Beginners to intermediate DevOps practitioners	SCM, DevOps fundamentals, tooling	Check website	https://www.scmgalaxy.com/
CLoudOpsNow.in	Cloud ops engineers, platform engineers	Cloud operations, SRE practices	Check website	https://www.cloudopsnow.in/
SreSchool.com	SREs, reliability engineers, ops leads	SRE principles, reliability, monitoring/alerting	Check website	https://www.sreschool.com/
AiOpsSchool.com	Ops/SRE teams exploring AIOps	AIOps concepts, automation, monitoring analytics	Check website	https://www.aiopsschool.com/

19. Top Trainers

Platform/Site Name	Likely Specialization	Suitable Audience	Website URL
RajeshKumar.xyz	DevOps/Kubernetes training content (verify current offerings)	Engineers seeking instructor-led or guided learning	https://rajeshkumar.xyz/
devopstrainer.in	DevOps training and mentoring (verify course list)	Beginners to intermediate DevOps engineers	https://devopstrainer.in/
devopsfreelancer.com	Freelance DevOps help/training resources (verify services)	Teams needing short-term guidance	https://devopsfreelancer.com/
devopssupport.in	DevOps support and training resources (verify scope)	Ops teams needing hands-on assistance	https://devopssupport.in/

20. Top Consulting Companies

Company Name	Likely Service Area	Where They May Help	Consulting Use Case Examples	Website URL
cotocus.com	Cloud/DevOps consulting (verify current portfolio)	Platform engineering, Kubernetes adoption, CI/CD	Multicloud Kubernetes platform design, migration planning, operational readiness	https://cotocus.com/
DevOpsSchool.com	DevOps consulting and training services (verify offerings)	Kubernetes/DevOps enablement, team upskilling	Building standardized CI/CD pipelines, Kubernetes governance playbooks	https://www.devopsschool.com/
DEVOPSCONSULTING.IN	DevOps consulting (verify current offerings)	DevOps transformation, automation	GitOps rollout, monitoring strategy, cloud cost optimization	https://www.devopsconsulting.in/

21. Career and Learning Roadmap

What to learn before GKE on Azure

Kubernetes fundamentals:
Pods, Deployments, Services, Ingress
ConfigMaps, Secrets
Storage (PV/PVC), StatefulSets
RBAC and namespaces
Google Cloud fundamentals:
Projects, IAM, service accounts
Cloud Logging/Monitoring basics
Networking basics (VPC concepts)
Azure fundamentals:
Subscriptions, resource groups
VNets/subnets, NSGs
Azure IAM/RBAC and service principals/app registrations
Azure load balancers and VM sizing

What to learn after GKE on Azure

Fleet governance at scale (cluster inventory, policy baselines)
GitOps operations and change management
Service mesh patterns (if you adopt a mesh; verify compatibility)
Observability engineering (SLOs, alerting, tracing)
Multicloud networking and egress cost management
Kubernetes security hardening and compliance workflows

Job roles that use it

Platform Engineer / Staff Platform Engineer
Cloud Engineer (multicloud)
DevOps Engineer
Site Reliability Engineer (SRE)
Security Engineer (cloud/Kubernetes)
Solutions Architect

Certification path (if available)

Google Cloud certifications that align well:
Professional Cloud Architect
Professional Cloud DevOps Engineer
Kubernetes certification:
CKA / CKAD (CNCF)
Azure certification (helpful for the Azure runtime side):
Azure Administrator Associate
Azure Solutions Architect Expert

There is not always a service-specific certification for GKE on Azure; validate current certification offerings in official channels.

Project ideas for practice

Build a “platform baseline” repo that applies:
Namespaces, RBAC, network policies, resource quotas
Ingress standardization
Implement a multi-environment layout:
dev/stage/prod clusters, each with a node pool strategy
Create a cost dashboard:
Map Azure tags + Kubernetes namespaces to cost centers
Design a DR exercise:
Restore workloads and data from backups into a fresh cluster

22. Glossary

GKE on Azure: Google-managed Kubernetes clusters running on Microsoft Azure infrastructure.
Fleet: A Google Cloud construct to group and govern multiple Kubernetes clusters consistently.
Multicloud: Using multiple cloud providers (for example, Google Cloud and Azure) in one platform strategy.
Hybrid: Combining cloud and on-prem environments in one operating model.
Node pool: A group of worker nodes with the same configuration used for scaling and workload separation.
Kubernetes RBAC: Role-based access control inside Kubernetes, controlling who can perform actions on resources.
Azure Resource Group: A logical container for Azure resources that share lifecycle/permissions.
VNet: Azure virtual network used to isolate and route traffic for cluster components.
Service principal: An Azure identity used by applications/services to authenticate and manage Azure resources.
Ingress: Kubernetes API object/pattern for routing HTTP(S) traffic to services (implementation depends on controller).
Service (LoadBalancer): A Kubernetes service type that provisions a cloud load balancer to expose a service externally.
Egress: Outbound network traffic; cross-cloud egress often incurs significant cost.
SLO: Service Level Objective; a target reliability level for a service (latency, availability, error rate).
GitOps: Managing infrastructure and app configuration via Git as the source of truth, with automated reconciliation.

23. Summary

GKE on Azure is Google Cloud’s managed Kubernetes offering for running GKE clusters on Azure infrastructure, positioned within Google Cloud’s Distributed, hybrid, and multicloud portfolio. It’s most valuable when you need Azure runtime residency but want consistent GKE-style operations, governance, and (optionally) fleet-based controls across environments.

Cost-wise, plan for two layers of spend: Google Cloud-side licensing/management and Azure-side infrastructure, plus potential cross-cloud egress and observability ingestion costs. Security-wise, success depends on disciplined least-privilege IAM in both clouds, strong network controls, and careful handling of credentials and audit logs.

Use GKE on Azure when multicloud standardization and centralized governance matter and you’re prepared for two-cloud operations. If you’re exclusively Azure-focused and want the simplest Azure-native experience, AKS may be the better fit.

Next step: read the current official docs entry point and follow the latest “Create a cluster” guide end-to-end, then repeat this tutorial’s deployment/validation/cleanup workflow until it becomes operational muscle memory: https://cloud.google.com/kubernetes-engine/multi-cloud/docs/azure

Category

1. Introduction

2. What is GKE on Azure?

Official purpose

Core capabilities

Major components

Service type

Scope (regional/global/project/subscription)

How it fits into the Google Cloud ecosystem

3. Why use GKE on Azure?

Business reasons

Technical reasons

Operational reasons

Security/compliance reasons

Scalability/performance reasons

When teams should choose GKE on Azure

When teams should not choose it

4. Where is GKE on Azure used?

Industries

Team types

Workloads

Architectures

Real-world deployment contexts

Production vs dev/test usage

5. Top Use Cases and Scenarios

1) Centralized Kubernetes governance for Azure-hosted workloads

2) Regulated workload must stay in Azure, but ops team standardizes on GKE

3) M&A platform consolidation across clouds

4) Multicloud disaster recovery (DR) for Kubernetes services

5) Data gravity in Azure + centralized ops in Google Cloud

6) Standardized GitOps across multicloud clusters

7) Security posture management across clusters

8) Blue/green platform upgrades with multiple clusters

9) Standardized developer experience for multiple environments

10) Edge-adjacent workloads executed in Azure regions

6. Core Features

Managed Kubernetes clusters on Azure

Cluster lifecycle management (create/upgrade/delete)

Node pools (worker capacity management)

Integration with Google Cloud fleet management

Observability integrations (logging/monitoring) where supported

Policy and configuration management (GKE Enterprise options)

Networking and load balancing using Azure primitives

Role-based access control (RBAC) and identity integration

7. Architecture and How It Works

High-level architecture

Control flow and data flow

Integrations with related services

Dependency services

Security/authentication model (conceptual)

Networking model (conceptual)

Monitoring/logging/governance considerations

Simple architecture diagram

Production-style architecture diagram

8. Prerequisites

Accounts, projects, and subscriptions

Permissions / IAM roles

Billing requirements

Tools and CLIs

Region availability

Quotas / limits

Prerequisite services

9. Pricing / Cost

Official pricing sources (start here)

Pricing dimensions (what you are billed for)

Google Cloud side

Azure side

Cost drivers (what makes bills go up)

Hidden or indirect costs to plan for

Network / data transfer implications

How to optimize cost

Example low-cost starter estimate (conceptual)

Example production cost considerations

10. Step-by-Step Hands-On Tutorial

Objective

Lab Overview

Step 1: Create/select a Google Cloud project and enable billing

Step 2: Install required CLIs locally

Step 3: Enable required Google Cloud APIs (console-driven, safest)

Step 4: Prepare Azure: login and select subscription

3) `kubectl get nodes` hangs or cannot reach cluster