Multi-Cloud DevOps: Manage Infrastructure Across AWS, Azure, and GCP (2026)

The real challenge of multi-cloud DevOps isn’t about orchestrating deployments; it’s about wrestling with the fundamental, irreconcilable differences in how each cloud provider thinks about resources and identity.

Let’s look at a typical scenario: deploying a simple web application across AWS, Azure, and GCP.

AWS:

Imagine you’re spinning up an EC2 instance to host your app. You’d likely use Terraform:

resource "aws_instance" "web_server" {
  ami           = "ami-0abcdef1234567890" # Example AMI ID
  instance_type = "t3.micro"
  subnet_id     = "subnet-0123456789abcdef0" # Example subnet ID
  vpc_security_group_ids = ["sg-0abcdef1234567890"] # Example security group ID
  tags = {
    Name = "HelloWorldWebServer"
  }
}

Here, ami is a specific Amazon Machine Image, instance_type defines the compute resources, subnet_id places it within a virtual network, and vpc_security_group_ids controls network access. It’s a very direct, almost server-like abstraction.

Azure:

Now, let’s do the same on Azure. You’d use Azure CLI or Terraform with the Azure provider. With Terraform:

resource "azurerm_virtual_machine" "web_server" {
  name                  = "helloworldwebserver"
  location              = "East US"
  resource_group_name   = "helloworld-rg"
  network_interface_ids = [azurerm_network_interface.main.id]
  vm_size               = "Standard_B1ms"

  storage_image_reference {
    publisher = "Canonical"
    offer     = "UbuntuServer"
    sku       = "18.04-LTS"
    version   = "latest"
  }

  os_disk {
    caching              = "ReadWrite"
    storage_account_type = "Standard_LRS"
  }
}

Notice the differences. Instead of an ami, you have storage_image_reference with publisher, offer, sku, and version. The instance size is vm_size. Resources are grouped into resource_group_name. It’s more about declarative configurations and resource grouping.

GCP:

On Google Cloud, using Terraform:

resource "google_compute_instance" "web_server" {
  name         = "helloworldwebserver"
  machine_type = "e2-micro"
  zone         = "us-central1-a"

  boot_disk {
    initialize_params {
      image = "ubuntu-os-cloud/ubuntu-2004-lts" # Example image
    }
  }

  network_interface {
    network = "default"
    access_config {
      // Ephemeral IP
    }
  }
}

Here, machine_type is the equivalent of instance size, zone is the deployment region, and boot_disk references an image. The networking is also handled differently, with network_interface. GCP often feels more "managed service" oriented, even for VMs.

The Mental Model:

The core problem multi-cloud DevOps solves is abstracting these differences. You’re not just deploying to a cloud; you’re deploying across them. This requires a layer of abstraction that can translate your desired state into the specific APIs and resource models of each provider. Tools like Terraform, Pulumi, or Crossplane are essential here. They allow you to define your infrastructure declaratively once and then apply it to any of the supported clouds.

The mental model shifts from "how do I create a VM in AWS?" to "how do I declare a compute instance that can run my app, and then let the chosen provider figure out the specifics?" This involves understanding:

• Resource Abstraction: How do you represent a virtual machine, a database, or a load balancer in a way that’s portable?
• State Management: How do you track what’s deployed and where, especially when resources are managed by different APIs?
• Identity and Access Management (IAM): This is the real beast. Each cloud has its own complex IAM system (AWS IAM, Azure AD, GCP IAM). A unified approach requires mapping your organizational roles and permissions to each provider’s model, which is rarely a one-to-one mapping.
• Networking: Subnets, VPCs, security groups, firewalls – these concepts exist everywhere but are implemented and configured differently.

The Undocumented Nuance:

What most people miss is how resource naming conventions and lifecycle management differ. In AWS, you might delete a security group and it’s gone. In Azure, deleting a resource group might not immediately delete all its constituent resources, leading to orphaned objects if not carefully managed. Similarly, image versioning and availability vary wildly. A latest tag in one cloud might point to a vastly different underlying OS version or patch level than in another, leading to subtle application behavior changes.

The next hurdle you’ll face is managing secrets and sensitive configuration data consistently across these disparate environments.