AI has made it much easier to create static pages.

A landing page can have five different versions in the same afternoon. A client preview can be regenerated several times. A product idea can go from prompt to working HTML in minutes.

The interesting problem is that deployment is no longer the slowest part.

The new problem is preview debt.

You generate a page, deploy it, share the URL, make a few changes, deploy another version, and repeat. After a while, you have multiple temporary projects, old URLs, duplicated files, and no clear idea which version was actually approved.

For production projects, Git history and a structured deployment workflow solve this well.

But for temporary pages, quick experiments, and client reviews, the process can become messy very quickly.

I have been thinking about a lighter workflow:

1. Generate the HTML

2. Preview it before publishing

3. Check the page on different screen sizes

4. Publish only the version worth sharing

5. Keep a simple history of the pages that were actually deployed

The goal is not just faster deployment. It is reducing the number of unnecessary deployments created during experimentation.

I started testing this idea in HTML Deployer, a small browser tool that lets users preview AI-generated HTML before publishing it to Vercel or another hosting target.

Project link for context:

https://backrun.co/html-deployer

I am curious how other people handle this today:

Do you regularly create temporary Vercel projects for client previews or AI experiments?

How do you keep track of which preview URL is the current one?

Do old preview projects become clutter over time?

Would you prefer to preview locally in the browser and deploy only the final candidate?

Interested to hear how others manage fast experiments without creating a large number of disposable deployments.