Generative AI Deployment with Terraform

 

A Multi-Cloud Comparison

This post provides a detailed breakdown of the steps and resources required to deploy a generative AI application using Terraform, drawing on the provided Google Cloud blog post and comparing the process to Azure and AWS.

Part 1: The Google Cloud (GCP) Approach (Based on the Blog Post)

The blog post "Deploy a Generative AI Application with Terraform" focuses on using a specific set of GCP services and Terraform resources. The goal is to set up a serverless application that can interact with a large language model.

Core Services Used

• Generative AI on Vertex AI: This is Google Cloud's fully managed platform for machine learning and AI development. It provides access to Google's foundation models.

• Cloud Functions: A serverless compute service that allows you to run code without provisioning or managing servers. It will host the application's back-end logic.

• Cloud Storage: Used for storing the application's code and dependencies.

Terraform Resources & Files

• main.tf: The primary configuration file where you define all the resources.

• google_project: Represents the GCP project.

• google_service_account: Creates a service account for the Cloud Function to run with.

• google_storage_bucket: Provisions the Cloud Storage bucket.

• google_storage_bucket_object: Uploads the Cloud Function code to the bucket.

• google_cloudfunctions2_function: Defines the Cloud Function itself, pointing to the code in the storage bucket.

• google_cloud_run_service_iam_member: Sets the IAM policy to allow public access to the Cloud Function endpoint.

• variables.tf: Contains all the input variables for your configuration, such as the project ID and region.

• outputs.tf: Defines the output values, such as the URL of the deployed Cloud Function, so you can easily access them after deployment.

Deployment Steps

1. Prerequisites:

• Install the gcloud CLI.

• Install Terraform.

• Authenticate with Google Cloud using gcloud auth application-default login.

2. Code: Create the Terraform configuration files (main.tf, variables.tf, outputs.tf) and the application code for the Cloud Function.

3. Initialization: Run terraform init to initialize the working directory and download the necessary providers.

4. Planning: Run terraform plan to see a preview of the infrastructure changes that will be made.

5. Deployment: Run terraform apply to create the resources in your GCP project. Terraform will execute the plan and output the Cloud Function's URL upon completion.

Part 2: Comparison with Azure & AWS

Azure

Azure's approach to generative AI deployment with Terraform centers on its Azure AI services, particularly Azure OpenAI Service. The steps are conceptually similar but use different resources and services.

• Generative AI Service: The primary service is Azure OpenAI Service, which provides access to models like GPT-4.

• Serverless Compute: Azure Functions is the direct equivalent of GCP Cloud Functions.

• Storage: Azure Blob Storage or Azure Data Lake Storage are used for storing code and data.

GCP Resource / Service	Azure Equivalent	Description
google_project	azurerm_resource_group	A logical container for all your resources.
google_storage_bucket	azurerm_storage_account	Stores your application code, model data, etc.
google_cloudfunctions2_function	azurerm_function_app	Hosts the serverless back-end code.
Vertex AI / Generative AI	azurerm_cognitive_account	The resource that provisions and manages the Azure OpenAI service.
gcloud auth	az login	The command-line tool for authenticating with the cloud provider.

AWS

AWS provides a highly flexible environment for generative AI. The approach with Terraform typically involves using a combination of services, with Amazon Bedrock often serving as the AI backbone.

• Generative AI Service: Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models.

• Serverless Compute: AWS Lambda is the serverless function service, analogous to Cloud Functions and Azure Functions.

• Storage: Amazon S3 (Simple Storage Service) is the object storage service used for code, data, and model artifacts.

• API Endpoint: Amazon API Gateway is commonly used to create a REST API endpoint for the Lambda function.

GCP Resource / Service	AWS Equivalent	Description
google_project	AWS Account/Region	The main account and a selected region to host resources.
google_storage_bucket	aws_s3_bucket	The storage service for application code and data.
google_cloudfunctions2_function	aws_lambda_function	The serverless compute service that runs the application logic.
Vertex AI / Generative AI	Amazon Bedrock (via API)	Bedrock is a service, and you'd use a Lambda function with appropriate IAM roles to interact with it via API calls.
gcloud auth	aws configure	The command-line tool for setting up authentication.

Summary of Steps Across Clouds

Step	GCP (Google Cloud)	Azure	AWS
Authentication	gcloud auth application-default login	az login	aws configure
Provider	hashicorp/google	hashicorp/azurerm	hashicorp/aws
Resource Grouping	google_project	azurerm_resource_group	N/A (Resources are in a region)
Core AI Service	google_cloudfunctions2_function	azurerm_cognitive_account	Interaction with Amazon Bedrock
Serverless Compute	google_cloudfunctions2_function	azurerm_function_app	aws_lambda_function
Storage	google_storage_bucket	azurerm_storage_account	aws_s3_bucket
IAM/Permissions	google_cloud_run_service_iam_member	azurerm_function_app_	aws_iam_role
Deployment Command	terraform apply	terraform apply	terraform apply