Deploy the Same App to AWS and Azure with Terraform

By Kokil Thapa | Last reviewed: August 2026

You want to deploy the same app to AWS and Azure with Terraform without maintaining two completely separate codebases or drowning in conditional logic. The challenge isn't writing infrastructure code; it's structuring that code so cloud-specific differences don't leak into your application architecture. For teams managing clients across regions or needing disaster recovery across providers, this abstraction is mandatory, not optional. If you are evaluating cloud hosting versus traditional shared hosting, understanding this portability layer helps justify the initial complexity investment against long-term operational resilience.

How do you structure Terraform code to deploy the same app to AWS and Azure?

The most common mistake engineers make when trying to deploy the same app to AWS and Azure with Terraform is attempting to write a single "universal" module that contains both aws_instance and azurerm_linux_virtual_machine resources with count or for_each toggles. This creates an unmaintainable mess of conditionals that breaks whenever either provider updates. In practice, on production systems I maintain, the reliable pattern is interface-driven abstraction. You define what the application needs (compute, database, cache, ingress) in a contract, then fulfill that contract separately for each cloud.

This directory structure enforces that separation physically:

infrastructure/
├── modules/
│   └── web-app-interface/
│       ├── variables.tf      # Normalized inputs only
│       ├── outputs.tf        # Cloud-agnostic endpoints
│       └── main.tf           # Validation rules, no resources
├── environments/
│   ├── aws-prod/
│   │   ├── main.tf           # Calls aws-specific implementation
│   │   ├── backend.tf        # S3/DynamoDB state config
│   │   └── terraform.tfvars
│   └── azure-prod/
│       ├── main.tf           # Calls azurerm-specific implementation
│       ├── backend.tf        # Azure Blob state config
│       └── terraform.tfvars
└── implementations/
    ├── aws-web-app/          # Maps interface → AWS resources
    └── azure-web-app/        # Maps interface → Azure resources

The web-app-interface module contains zero cloud resources. It defines variables like app_cpu_cores, app_memory_gb, db_engine_version, and ingress_domain. Each implementation module translates these into provider-native equivalents. On AWS, 2 CPU cores might map to a t4g.large; on Azure, to a Standard_B2ms. The calling root module never knows or cares about this translation.

What are the key differences when deploying Laravel apps to AWS vs Azure?

When you deploy the same app to AWS and Azure with Terraform, the application code stays identical but the infrastructure primitives diverge significantly. Having deployed Laravel applications on both platforms for legal-tech portals and eCommerce systems, I've documented the friction points that consistently cause issues during migration or dual-deployment setups. Understanding these before writing Terraform prevents costly rewrites later.

The biggest gotcha in 2026 remains identity federation. AWS IAM roles can be assumed by services with minimal ceremony. Azure Managed Identities require explicit role assignments on each target resource, and Terraform's azurerm_role_assignment often needs depends_on to avoid race conditions during initial deployment. Always test identity propagation in isolation before wiring it into your full application stack.

How do you manage Terraform state for multi-cloud deployments?

State management is where multi-cloud projects fail silently. You must never store AWS and Azure state in the same backend or workspace. When you deploy the same app to AWS and Azure with Terraform, each cloud's state file contains provider-specific resource IDs, ARNs, or resource paths that are meaningless to the other provider. Mixing them creates import conflicts and makes disaster recovery impossible.

Configure backends explicitly in each environment's backend.tf:

# environments/aws-prod/backend.tf
terraform {
  backend "s3" {
    bucket         = "myapp-tfstate-prod"
    key            = "aws/web-app/terraform.tfstate"
    region         = "ap-south-1"
    dynamodb_table = "myapp-tfstate-lock"
    encrypt        = true
  }
}

# environments/azure-prod/backend.tf
terraform {
  backend "azurerm" {
    resource_group_name  = "rg-terraform-state"
    storage_account_name = "tfstatemyappprod"
    container_name       = "tfstate"
    key                  = "azure/web-app/terraform.tfstate"
  }
}

For CI/CD integration, which I cover more deeply in my notes on setting up CI/CD pipelines for production deployments, always run separate pipeline jobs for each cloud. Never use a single job that iterates over clouds. Parallel execution is fine; shared context is not. If you're coordinating deployments across both clouds from a single orchestrator, pass outputs between them via external APIs or artifact stores, not Terraform remote state references.

How do you handle provider-specific networking and security in Terraform?

Networking is the least portable layer when you deploy the same app to AWS and Azure with Terraform. VPCs and VNets share conceptual DNA but differ in CIDR handling, subnet association, and security group semantics. Writing truly abstracted networking modules is possible but often counterproductive. Instead, accept that networking will be provider-specific and isolate it behind stable outputs.

1. Define network requirements in the interface module: Specify needed subnets (public, private, database), CIDR ranges, and connectivity rules as abstract variables. Don't specify AWS security group IDs or Azure NSG names here.
2. Implement networking natively per cloud: AWS uses VPC + Internet Gateway + NAT Gateway + Security Groups. Azure uses VNet + Bastion/Subnet delegation + NSGs + Private Endpoints. Let each implementation use idiomatic patterns.
3. Expose normalized outputs: Both implementations should output private_subnet_ids, database_subnet_ids, and allowed_ingress_cidrs using identical types. The application module consumes these without knowing their origin.
4. Validate at plan time: Use Terraform 1.9+ validation blocks and precondition checks to ensure outputs conform to the interface contract before apply.

Security identity deserves special attention. On AWS, your Laravel app might assume an IAM role to access S3. On Azure, it uses a User-Assigned Managed Identity attached to the VM or Container App. The Terraform variable storage_access_identity resolves to an IAM role ARN in AWS and a Managed Identity principal ID in Azure. Your application code reads a generic environment variable like CLOUD_STORAGE_IDENTITY set by the provisioning script, keeping the runtime cloud-agnostic even if Terraform isn't.

What does a real multi-cloud deployment workflow look like?

Theory matters less than execution. Here's the workflow I use when helping teams deploy the same app to AWS and Azure with Terraform in production, particularly for clients who need geographic redundancy or are negotiating cloud contracts. This assumes Terraform 1.9+ and provider versions current as of mid-2026.

Critical implementation details that prevent production incidents:

• Never share .tfvars files between clouds. Even if values look identical today, they will diverge. Maintain aws-prod.tfvars and azure-prod.tfvars separately. Use a shared common.tfvars only for truly cloud-agnostic values like application version tags or team owner labels.
• Pin provider versions exactly. In 2026, AWS provider 5.x and AzureRM provider 4.x have breaking changes between minor versions. Your required_providers block must specify exact versions, not ranges. Multi-cloud state corruption from provider upgrades is painful to diagnose.
• Test interface contracts with Terratest or similar. Write Go tests that instantiate your interface module with mock values and verify both AWS and Azure implementations produce valid plans. This catches abstraction leaks before they reach production.
• Document the abstraction boundary. Maintain a DECISIONS.md file explaining why certain resources are abstracted and others aren't. Future engineers (including yourself at 2 AM) need to understand why networking wasn't unified or why secrets management differs.

If you're building this for a client in Nepal or South Asia, consider latency and data residency early. AWS Mumbai (ap-south-1) and Azure Central India (pune) are typical choices, but Azure's newer Hyderabad region may offer better pricing for certain workloads. Factor regional availability into your interface module's variable validation — there's no point abstracting a service that doesn't exist in your target region. For teams evaluating whether custom cloud infrastructure makes sense versus managed platforms, my comparison of no-code versus custom development approaches covers the trade-offs relevant to this decision.

Deploy the Same App to AWS and Azure with Terraform: Next Steps

Successfully implementing this pattern requires disciplined module design, strict state isolation, and acceptance that some cloud differences cannot be abstracted away. Start with the interface module pattern described above, validate it with a minimal workload, and expand incrementally. Resist the urge to build a universal abstraction layer prematurely; let real deployment pain drive abstraction decisions. If your team needs hands-on support architecting multi-cloud Terraform workflows or migrating existing single-cloud deployments to a portable foundation, reach out to discuss your specific infrastructure requirements.