Deploying Terraform IAC Using Azure DevOps Runtime Parameters

While deploying your same terraform code manually multiple times you must have got through the thoughts:

  • If we can automate the whole deployment process and replace the whole tedious process with few clicks.
  • If we can dynamically change the values of terraform.tfvars.
  • If we can restrict the regions of deployments.
  • If we can limit our VM types to maintain better cost optimization.

In this article, we will touch upon these problems and try to resolve them in a way that the same concepts can also be applied to similar requirements.

Soo… Let’s Get Started !!!

First of all, we need to know what is Terraform & Azure DevOps.

Talking About Terraform: HashiCorp Terraform is an infrastructure as a code tool that lets you define both cloud and on-prem resources in human-readable configuration files that you can version, reuse, and share. You can then use a consistent workflow to provision and manage all of your infrastructures throughout its cycle. Terraform can manage low-level components like compute, storage, and networking resources, as well as high-level components like DNS entries and SaaS features.

Terraform Workflow

Talking about Azure DevOps: Azure DevOps provides developer services for allowing teams to plan work, collaborate on code development, and build and deploy applications. Azure DevOps supports a collaborative culture and set of processes that bring together developers, project managers, and contributors to develop software. It allows organizations to create and improve products at a faster pace than they can with traditional software development approaches.

DevOps lifecycle in Azure DevOps

If you want to learn more about Azure DevOps click here.


No matter whether we are deploying our infrastructure into Azure Cloud Services or Amazon Web Services (AWS). All we need are the following checklist:

  • Active Cloud Service (Azure/AWS)
  • Azure DevOps Account
  • Terraform Code to deploy.
  • A Linux machine (VM or EC2) for agent pool or Azure Microsoft-hosted agent.
  • Storage Account (Azure Blob Container or AWS S3)
  • Terraform code to deploy using terraform.tfvars.

Azure DevOps Pipeline

Let’s take a scenario in which we will deploy a simple terraform code of Azure Virtual Machine using Azure DevOps pipelines.

Have a look at the

resource "azurerm_resource_group" "rg" {
  name     = "dev-${}-rg"
  location = var.region

resource "azurerm_virtual_network" "vnet" {
  name                = "dev-${}-vnet"
  address_space       = [""]
  location            = azurerm_resource_group.rg.location
  resource_group_name =

resource "azurerm_subnet" "subnet" {
  name                 = "dev-${}-subnet"
  resource_group_name  =
  virtual_network_name =
  address_prefixes     = [""]

resource "azurerm_network_interface" "nic" {
  name                = "dev-${}-nic"
  location            = azurerm_resource_group.rg.location
  resource_group_name =

  ip_configuration {
    name                          = "dev-${}-ip"
    subnet_id                     =
    private_ip_address_allocation = "Dynamic"

resource "azurerm_linux_virtual_machine" "vm" {
  name                  = "dev-${}-vm"
  resource_group_name   =
  location              = azurerm_resource_group.rg.location
  size                  = var.vm_size
  admin_username        = "ubuntu"
  network_interface_ids = [,

  admin_ssh_key {
    username   = "dev-${}-key"
    public_key = file("~/.ssh/")

  os_disk {
    caching              = "ReadWrite"
    storage_account_type = var.vm_storage_account_type

  source_image_reference {
    publisher = "Canonical"
    offer     = "UbuntuServer"
    sku       = var.image_sku
    version   = "latest"

Let’s have a look at the terraform.tfvars file.

name                    = "{vm}"

region                  = "{West Europe}"

vm_size                 = "{StandardF2}"

vm_storage_account_type = "{StandardLRS}"

image_sku               = "{16.04-LTS}"

Pipeline Parameters

Let’s pass the following values dynamically using pipeline parameters.

  1. Name of VM and other resources.
  2. Regions of deployment.
  3. Size of VM.
  4. VM storage account type.
  5. VM image SKU
  - name: name
    displayName: Name_of_Resource
    type: string
    default: application
  - name: region
    displayName: region
    type: string
    default: eastus
    - eastus
    - eastus2
    - northeurope
    - centralindia

  - name: vmSize
    displayName: VM_Size
    type: string
    default: D4s_v3
    - D2as_v4
    - DS2_v2
    - D4s_v3
    - D2as_v4
    - DS3_v2
    - D8s_v3

  - name: vmStorageAccountType
    displayName: VM_Storage_Account_Type
    type: string
    default: Standard_LRS
    - Standard_LRS
    - StandardSSD_LRS
    - Premium_LRS
    - UltraSSD_LRS

  - name: imageSKU
    displayName: Image_SKU
    type: string
    default: 20.04-LTS
    - 16.04-LTS
    - 18.04-LTS
    - 20.04-LTS
    - 22.04-LTS

In these pipeline parameters, we’re also restricting/limiting the range of values by providing a list of values to our parameters. In this way, the user cannot go beyond these pre-defined values while executing the pipeline.

Pipeline Steps:

In our pipeline, ‘we will use the below-mentioned steps

1. Replacing Values

- bash: |
    sed -i "s/{vm}/${{ }}/g" terraform.tfvars
    sed -i "s/{West Europe}/${{ parameters.region }}/g" terraform.tfvars
    sed -i "s/{StandardF2}/${{ parameters.vmSize }}/g" terraform.tfvars
    sed -i "s/{StandardLRS}/${{ parameters.vmStorageAccountType }}/g" terraform.tfvars
    sed -i "s/{16.04-LTS}/${{ parameters.imageSKU }}/g" terraform.tfvars
    cat terraform.tfvars
  displayName: 'Replace Values'

This is the heart of our pipeline. In this step, we are using the terraform azure pipeline parameters.

2. Terraform Tool Installer

- task: TerraformInstaller@0
    terraformVersion: 'latest'
  displayName: 'Install Terraform latest'

In this step, we will install terraform tool for our pipeline.

3. Terraform Init

- task: TerraformTaskV3@3
    provider: 'azurerm'
    command: 'init'
    backendServiceArm: 'Opstree-PoCs (4c93adXXXXXXXXXXXXXXXXXXXXXX8f3c)'
    backendAzureRmResourceGroupName: 'jenkins_server'
    backendAzureRmStorageAccountName: 'asdfghjkasdf'
    backendAzureRmContainerName: 'backend'
    backendAzureRmKey: 'backend.tfstate'

This step will initialize the terraform code and the terraform backend configuration.

4. Terraform Validate

- task: TerraformTaskV3@3
  displayName: 'Terraform : Validate'
    command: validate

In this step, we will validate our terraform code configuration

5. Terraform Plan

- task: TerraformTaskV3@3
  displayName: 'Terraform : Plan'
    provider: 'azurerm'
    command: 'plan'
    commandOptions: '-lock=false'
    environmentServiceNameAzureRM: 'Opstree-PoCs (4c9xxxxxxxxxxx3c)'

This step will caution the instructor for sure.

6. Terraform Apply

- task: TerraformTaskV3@3
    provider: 'azurerm'
    command: 'apply'
    commandOptions: '-auto-approve'
    environmentServiceNameAzureRM: 'Opstree-PoCs (4c93xxxxxxxxf3c)'

This step will execute the configuration file and launch a VM instance. When you run apply command, it will ask you, “Do you want to perform these actions?”, you need to type yes and hit enter. To skip that we have provided our configuration with an “-auto-approve” argument.

Upon saving and running our pipeline we can choose our desired parameters in this way.

We will get a drop-down for each parameter whose value we restricted.


So far we’ve learned how to make the pipeline for our terraform code using Azure DevOps Pipelines. Along with that, we’ve found out how to pass the runtime parameters to dynamically give values to our terraform.tfvars file and also restrict or limit the values as per our requirements.

Content References Reference 1, Reference 2
Image ReferencesImage 1, Image 2

Thanks for reading this article! I hope you benefited from it.

Blog Pundits: Mehul Sharma and Sandeep Rawat

Opstree is an End to End DevOps solution provider.

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