Ask an image model for the same poster twice and you get two posters. Usually that is the point. For a sale campaign going out across four hundred store pages, it is the problem.

The failure mode nobody screenshots

Generative output fails in ways that look fine in isolation. A logo that is 4% wider than the brand guide. A price that lands two pixels into the safe area. A legal disclaimer that quietly does not appear on run 340 of 400.

None of these look like errors when you spot-check ten of them. All of them are errors, and the ones that reach a customer are the ones you did not check.

What a rendering engine does instead

A template fixes the layout. The API fills in the fields you exposed and nothing else. There is no sampling step, no temperature, no seed to chase — rendering is a pure function of your template and your data.

That gives you a property worth more than novelty:

The same input produces byte-identical output, months apart
Only fields you exposed can change; logo, colours and safe areas are locked
A failed render costs nothing, because there is nothing to re-roll

Predictability is not a limitation of the approach. It is the entire product.

Where generative tools genuinely win

This is not an argument that models are useless. They are excellent at the upstream problem — producing the source imagery, removing a background, extending a photograph to a new aspect ratio.

Use them there. Then hand the result to something that will place it in exactly the same position, at exactly the same size, ten thousand times.

The cost argument, briefly

There is also a straightforward economic case. A poster rendered from a template costs milliseconds of browser time. The same poster generated by a model costs GPU inference, every single time, forever.

That difference is why a template-driven free tier can be genuinely free, and why per-asset generative pricing tends not to survive contact with a real catalogue.