Managing AI-Ready Infrastructure in Microsoft Azure using HashiCorp Terraform

Let’s be honest: deploying AI isn’t just about picking a model or calling an API. It’s about infrastructure. And that’s where so many projects stumble.

Picture this: your team is hyped about building a generative AI solution. You start spinning up VMs, storage accounts, maybe a GPU-enabled cluster. Suddenly your Azure portal is a mess — clutter everywhere, no one remembers who owns what, and good luck replicating that environment in production.

Sound familiar? You’re not alone.

That’s why we need AI-Ready Infrastructure — a repeatable, scalable, secure foundation that makes AI projects not only possible, but sustainable. And, what’s the best ways to manage it? Infrastructure as Code (IaC) and HashiCorp Terraform.

Before we dive into code, let’s step back and define what AI-Ready Infrastructure really means. (And if you want the deep dive, check out my book Designing AI-Ready Infrastructure in Microsoft Azure. )

TL;DR: What You’ll Learn

• What AI-Ready Infrastructure is and why it matters.

• How Terraform helps tame the complexity of Azure resources.

• Example Terraform snippets for networking and AI services.

• Pro tips to avoid common pitfalls when building for AI workloads.

🧩 What Is AI-Ready Infrastructure?

Think of AI-Ready Infrastructure like the foundation of a skyscraper. If it’s shaky, everything above it crumbles.

In Azure, this means:

• Scalable compute (VMs, AKS, or managed AI services).

• High-performance networking (private endpoints, vNets, firewalls).

• Secure data storage (encrypted, governed, compliant).

• Monitoring & automation baked in from day one.

The wrong way? Treating AI like a one-off science experiment and building things ad-hoc.
The right way? Designing with repeatability, security, and governance in mind.

That’s where Infrastructure as Code (IaC) — and Terraform — shine.

🛠️ Terraform: Your Infrastructure Superpower

If you’ve ever tried to manually click through the Azure portal to configure AI workloads, you know the pain. It’s like assembling IKEA furniture without instructions — and then being told to make ten identical copies.

Terraform solves this by letting us declare infrastructure in code. Want a GPU cluster with private networking and monitoring? Write it once, version it in Git, deploy consistently across dev, test, and prod.

Here’s a quick Terraform starter for creating a virtual network to host AI workloads:

# Create a Resource Group
resource "azurerm_resource_group" "ai_rg" {
  name     = "rg-ai-infra"
  location = "East US"
}

# Virtual Network
resource "azurerm_virtual_network" "ai_vnet" {
  name                = "vnet-ai"
  address_space       = ["10.0.0.0/16"]
  location            = azurerm_resource_group.ai_rg.location
  resource_group_name = azurerm_resource_group.ai_rg.name
}

# Subnet for AI services
resource "azurerm_subnet" "ai_subnet" {
  name                 = "subnet-ai"
  resource_group_name  = azurerm_resource_group.ai_rg.name
  virtual_network_name = azurerm_virtual_network.ai_vnet.name
  address_prefixes     = ["10.0.1.0/24"]
}

This sets the stage: a clean, isolated network for your AI resources.

🤖 Adding AI Services with Terraform

Now let’s drop in some AI capability. Azure AI services (like Azure OpenAI, Cognitive Services, or Machine Learning) can all be provisioned with Terraform. Here’s a simplified example using Azure Cognitive Services:

# Cognitive Services Account
resource "azurerm_cognitive_account" "ai_services" {
  name                = "cog-ai-demo"
  location            = azurerm_resource_group.ai_rg.location
  resource_group_name = azurerm_resource_group.ai_rg.name
  kind                = "CognitiveServices"
  sku_name            = "S0"

  network_acls {
    default_action = "Deny"
    virtual_network_rules {
      subnet_id = azurerm_subnet.ai_subnet.id
    }
  }
}

Notice a couple of things:

• We’re locking down access so only our subnet can reach this service.

• We’re using IaC, so this setup can be replicated in multiple environments with zero guesswork.

This is the magic of Terraform: it makes AI infrastructure not only possible, but predictable.

⚡ Pro Tips & Common Mistakes

• Don’t ignore networking. AI services often require low latency and high throughput. Misconfigured networking can kill performance.

• Version everything. Treat Terraform code like app code. Use Git. Review changes. Automate CI/CD.

• Start small, then scale. Don’t try to build the “perfect” AI infrastructure from day one. Begin with core services, then evolve.

• Secure by default. Use private endpoints, managed identities, and key vaults from the beginning. Retrofitting security later is painful.

🎯 Wrapping Up

Let’s recap:

• AI-Ready Infrastructure is the foundation of successful AI projects.

• Terraform gives us a powerful way to design, deploy, and manage this infrastructure in Azure.

• With just a few lines of HCL, you can spin up secure networking and AI services, ready for your next project.

The best part? Once you’ve defined it, you can scale it across environments, teams, and projects — without reinventing the wheel.

👉 Curious to go deeper? My book Designing AI-Ready Infrastructure in Microsoft Azure dives into architecture patterns, security models, and scaling strategies.