AI image generation can look like a simple request-response feature.

A user enters a prompt, clicks generate, and waits for an image.

For a prototype, that can work. For a production product, it usually becomes fragile.

Image generation may take several seconds or minutes. A provider may return a job ID first and the final result later. Some results arrive through webhooks. Others need polling. Requests can fail, time out, or finish after the user has already left the page.

That is why AI image generation is usually better designed as an asynchronous workflow.

This is the approach I use while building Image 2 , a multi-model AI image generation and editing platform.

The Simple Version

The most direct implementation looks like this:

User -> API route -> AI provider -> result -> user

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This is easy to understand, but it has several problems:

the HTTP request may time out

retries can create duplicate jobs

the frontend depends on provider latency

billing or…