LLamaRecipes/terraform/amazon-bedrock-default

Amazon Bedrock deployment

Deploy Llama 4 Scout models using Amazon Bedrock managed service.

Overview

This Terraform configuration sets up a basic example deployment, demonstrating how to deploy/serve Amazon Bedrock foundation models in Amazon Web Services. Amazon Bedrock provides fully managed AI models without any infrastructure management.

This example shows how to use basic services such as:

• IAM roles for permissions management
• Service accounts for fine-grained access control
• Access to Bedrock Llama models in a minimal policy

In our architecture patterns for private cloud guide we outline advanced patterns for cloud deployment that you may choose to implement in a more complete deployment. This includes:

• Deployment into multiple regions or clouds
• Managed keys/secrets services
• Comprehensive logging systems for auditing and compliance
• Backup and recovery systems

Getting started

Prerequisites

• AWS account with access to Amazon Bedrock
• Terraform installed
• AWS CLI configured
• Model access enabled: Go to Amazon Bedrock console → Model access → Request access for Meta Llama models

Deploy

1. Configure AWS credentials:

   aws configure
   

2. Edit terraform.tfvars with your values.

3. Create configuration:

   cd terraform/amazon-bedrock-default
   cp terraform.tfvars.example terraform.tfvars
   

4. Deploy:

   terraform init
   terraform plan
   terraform apply
   

Usage

import boto3
import json

bedrock = boto3.client('bedrock-runtime', region_name='us-east-1')

response = bedrock.invoke_model(
    modelId='meta.llama4-scout-17b-instruct-v1:0',
    body=json.dumps({
        "prompt": "Hello, how are you?",
        "max_gen_len": 256,
        "temperature": 0.7
    })
)

Next steps

• Amazon Bedrock User Guide
• Amazon Bedrock API Reference