# Terraform: Getting Started with an Azure OpenAI Deployment

While working on Open AI for the past few months, I found a Terraform module that really helped me get started with the Azure OpenAI component.

The module,[Azure/openai/azurerm](https://registry.terraform.io/modules/Azure/openai/azurerm/latest), has already been built with the intention of customisation, scalability, and security. As such, this becomes a great module.

In this blog, I will demonstrate how to use the module to deploy a public instance. Future blog posts will cover private network integration, adding a WAF, and further enhancements.

# Public Instance Deployment

The instance we are deploying will be on the internet (public). I would recommend increasing security by restricting networks from accessing the instance, something I will cover in future posts.

Below is a sample module block I use to deploy an instance. This alone will deploy a working OpenAI instance in an existing resource group:

```json
module "openai-uks" {
  source                        = "Azure/openai/azurerm"
  version                       = "0.1.3"
  location                      = "UK South"
  resource_group_name           = "Resource Group Name"
  account_name                  = "open-ai"
  sku_name                      = "S0"
  public_network_access_enabled = true
  deployment = {
    "gpt-3.5-turbo" = {
      name          = "gpt-35-turbo"
      model_format  = "OpenAI"
      model_name    = "gpt-35-turbo"
      model_version = "1106"
      scale_type    = "Standard"
      capacity      = 1
    },
    "gpt-4-turbo" = {
      name          = "gpt-4-turbo"
      model_format  = "OpenAI"
      model_name    = "gpt-4"
      model_version = "1106-Preview"
      scale_type    = "Standard"
      capacity      = 1
    }
  }
}
```

Let's break down what I have done in this module:

* **Source** - This is the location of the module we'll be using
    
* **Version** - The version of the module we are going to use
    
* **Location** - The location where we will deploy this resource. Please note that some models are not available in every region. You can find this by going to this [resource](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#standard-deployment-model-availability).
    
* **Resource\_group\_name** - The resource group name the resource will be logically located
    
* **Account\_name** - The name of the resource
    
* **Sku\_name** - The SKU you want to apply to the account.
    
* **Public\_network\_access\_enabled** - If the resources will be public accessible
    
* **Deployment** - This is where you will want to specify the models you want to deploy
    

You will see I have two models configured for deployment. Here is a breakdown of one of them to explain how you configure it:

* **Name** - The name you want to give your model. Can be anything
    
* **Model\_format** - The model format you want to use, so OpenAI is required here
    
* **Model\_name** - The name of the model. This can be GPT3.5, 3.5 Turbo, 4, etc...
    
* **Model\_version** - The version of the model you want to use. It is important to specify this to get specific releases, including preview/stable versions.
    
* **Scale\_type** - The scaling of the model
    
* **Capacity** - Token capacity. This is in thousands, so 1 equals 1,000.
    

# Outputs

When this instance deploys, there will be a couple of values you will want. You can get these from the portal, or you can output them as part of Terraform. I recommend outputting the following to an Azure Key Vault:

* **module.openai-uks.openai\_endpoint** - This will be the endpoint address
    
* **module.openai-uks.openai\_primary\_key** - This will be the primary key to authenticate with the instance
    
* **module.openai-uks.openai\_secondary\_key** - This will be the secondary key to authenticate with the instance
    

These values will allow you to connect to the OpenAI APIs for development.

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