Adding image generation to a product looks like a single API call and turns into a set of engineering decisions. These are the ones that keep coming up.
Latency budget. A 2-second generation is fine for a background job and unusable in a typing flow. Decide which one you are building before you pick a model.
Text inside images. Most models still mangle short strings. If your use case involves labels, packaging or UI mockups, test that specific case first - it eliminates more candidates than any benchmark.
Determinism and retries. If the same prompt returns a different image every time, you cannot cache; budget for it in both latency and cost.
Cost per accepted image. Count the rejects. A cheap model with a 40% accept rate is more expensive than a pricier one you rarely re-roll.
For teams that want an API-first route, services like ideogram api are worth a look because the interface is designed around that integration path rather than a consumer UI.
None of these checks need a prototype. An afternoon with a spreadsheet and ten prompts will tell you more than a month of comparing feature lists.
Disclosure: prepared with AI tooling assistance; the link is contextual and not sponsored.
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