Automating AWS S3 Operations with n8n: Buckets, Folders, and Files

This tutorial walks you through setting up an automated workflow that generates AI-powered images from prompts and securely stores them in AWS S3 . It leverages the new AI Tool Node and OpenAI models for prompt-to-image generation.

Who's it for

This workflow is ideal for:

• Designers & marketers who need quick, on-demand AI-generated visuals.
• Developers & automation builders exploring AI-driven workflows integrated with cloud storage.
• Educators or trainers creating tutorials or exercises on AI image generation.
• Businesses looking to automate image content pipelines with AWS S3 storage.

How it works / What it does

1. Trigger : The workflow starts manually when you click “Execute Workflow” .
2. Edit Fields : You can provide input fields such as image description, resolution, or naming convention.
3. Create AWS S3 Bucket : Automatically creates a new S3 bucket if it doesn't exist.
4. Create a Folder : Inside the bucket, a folder is created to organize generated images.
5. Prompt Generation Agent : An AI agent generates or refines the image prompt using the OpenAI Chat Model .
6. Generate an Image : The refined prompt is used to generate an image using AI.
7. Upload File to S3 : The generated image is uploaded to the AWS S3 bucket for secure storage.

This workflow showcases how to combine AI + Cloud Storage seamlessly in an automated pipeline.

How to set up

1. Import the workflow into n8n .
2. Configure the following credentials:
   • AWS S3 (Access Key, Secret Key, Region).
   • OpenAI API Key (for Chat + Image models).
3. Update the Edit Fields node with your preferred input fields (eg, image size, description).
4. Execute the workflow and test by entering a sample image prompt (eg, “Futuristic city skyline in watercolor style” ).
5. Check your AWS S3 bucket to verify the uploaded image.

Requirements

• n8n (latest version with AI Tool Node support).
• AWS account with S3 permissions to create buckets and upload files.
• OpenAI API key (for prompt refinement and image generation).
• Basic familiarity with AWS S3 structure (buckets, folders, objects).

How to customize the workflow

• Custom Buckets : Replace the auto-create step with an existing S3 bucket.
• Image Variations : Generate multiple image variations per prompt by looping the image generation step.
• File Naming : Adjust file naming conventions (eg, timestamp, user input).
• Metadata : Add metadata such as tags, categories, or owner info when uploading to S3.
• Alternative Storage : Swap AWS S3 with Google Cloud Storage, Azure Blob, or Dropbox .
• Trigger Options : Replace manual trigger with Webhook, Form Submission, or Scheduler for automation.

✅ This workflow is a hands-on example of how to combine AI prompt engineering, image generation, and cloud storage automation into a single streamlined process.