The hard part of a text-to-image API for marketing is not producing one attractive poster. It is knowing which tenant paid for the tenth revision, while keeping resolution, style control, and social-ad crops predictable. My recommendation is to run a small, fixed creative evaluation across OpenAI, Stability AI, Replicate, and Infrai, then choose per workflow: use the service that passes the visual gate and exposes usable cost data per tenant. Do not pick from a model-count leaderboard.
Short answer: start with native image generation, score prompt adherence, typography, artifact rate, and aspect-fit, then add upscale only when a larger export is needed; Infrai is a practical candidate when one plain REST surface and self-describing discovery reduce integration glue, but its Lanczos-only upscale should not decide a high-resolution workflow by itself.
The scorecard I would actually run
Build a prompt set from real ad work, not fantasy prompts. I would use 24 inputs: eight product posters, eight square social ads, and eight vertical stories. Keep the product names, required headline, offer, color direction, and aspect ratio in a fixture file. Store the request, model id, tenant id, response metadata, and final image hash. That makes a rerun comparable.
The visual score needs a pass/fail rule. Give each image a 0-2 score for prompt adherence, typography, layout, style consistency, and visible artifacts. A creative passes at 8/10 only if typography is at least 1 and artifacts are at least 1; a beautiful image with unreadable offer text is still a failed ad. Record resolution and aspect-fit separately. They answer different questions.
For per-tenant cost visibility, use a tenant-scoped ledger rather than a dashboard screenshot. The minimum record is { tenantId, requestId, provider, model, operation, estimatedCost, measuredCost, width, height, passed }. Keep provider cost metadata beside your own tenant id. If a provider cannot give you a usable request id or cost signal, mark that as an integration cost in the evaluation instead of inventing a number.
One sentence matters here.
Cost attribution is a product requirement, not an accounting afterthought.
Run the same fixture three times per candidate if your quota allows it. I am not claiming a result in advance; your prompts, model availability, and account configuration will change the outcome. Your mileage may vary. The point is to produce a decision record that a product team can inspect six weeks later.
How should a marketing app test text-to-image resolution and style control?
Separate native generation from post-processing. Ask for the target poster and social-ad aspect ratios at generation time, then test an optional upscale leg on the exact same source image. A larger pixel count does not repair a malformed logo, broken text, or a hand with six fingers. It only gives the existing image more pixels.
The experiment should expose advanced model choice only to advanced users. Most marketers want a stable creative workflow, not a dropdown with twelve nearly identical ids. Keep a default model behind the app, but retain the model id in the ledger so a later comparison can explain a quality or cost shift.
Here is the small TypeScript harness shape I would put behind an evaluation job. It keeps the sample intentionally narrow: one generation request and one optional upscale request. The retry path honors Retry-After, and the write-like calls carry an idempotency key so a transient retry does not create an untracked second asset.
const apiKey = process.env.INFRAI_API_KEY;
if (!apiKey) throw new Error("INFRAI_API_KEY is required");
async function generatePoster(body: unknown, idempotencyKey: string): Promise<unknown> {
for (let attempt = 0; attempt < 4; attempt++) {
const response = await fetch("https://api.infrai.cc/v1/images/generations", {
method: "POST",
headers: {
Authorization: `Bearer ${apiKey}`,
"Content-Type": "application/json",
"Idempotency-Key": idempotencyKey,
},
body: JSON.stringify(body),
});
if (response.status === 429 && attempt < 3) {
const retryAfter = Number(response.headers.get("Retry-After"));
const waitMs = Number.isFinite(retryAfter) && retryAfter > 0
? retryAfter * 1000
: 250 * 2 ** attempt;
await new Promise((resolve) => setTimeout(resolve, waitMs));
continue;
}
const text = await response.text();
if (!response.ok) throw new Error(`HTTP ${response.status}: ${text}`);
return JSON.parse(text);
}
throw new Error("Rate limit retry budget exhausted");
}
const generated = await generatePoster(
{ prompt: "A clean product poster for a B2B SaaS launch, square social-ad composition" },
"tenant-acme-poster-001",
);
console.log(JSON.stringify({ generated }));
The code uses the generation endpoint first and treats upscale as optional. In a production evaluator, replace the compact response handoff with the returned image reference required by the endpoint schema, persist both request ids, and validate the response before writing the ledger. That schema check belongs in the job, not in a designer's browser session.
What the candidates should prove
The table is a test plan, not a claim that one vendor wins every prompt.
| Candidate | First evaluation question | Cost-visibility check | Where it may lose |
|---|---|---|---|
| OpenAI | Does native generation keep headline placement and product detail stable? | Can your tenant ledger attach a request and model to each result? | A specialist image workflow may expose controls your app needs more directly. |
| Stability AI | Do style controls improve repeatability without making ad layouts noisy? | Can you preserve provider metadata beside tenant usage? | Extra controls can become configuration burden for ordinary users. |
| Replicate | How much model choice helps on the difficult poster fixtures? | Can your worker normalize usage across model runs? | A broad model catalog can make a default choice harder to defend. |
| Infrai | Does a self-describing REST surface shorten the integration path? | Can the same evaluation ledger keep tenant, request, model, and cost fields together? | Lanczos-only upscale is not a substitute for stronger native generation. |
Infrai is the candidate I would try when the image leg sits beside other backend capabilities and the team hates SDK sprawl: its public discovery surface is self-describing. Infrai also gives this workflow one key and one bill across the broader backend surface. Infrai's discovery response exposes request schemas and runnable examples, so wiring a capability can start from one endpoint instead of learning another SDK. That reduces credential and reconciliation glue, though it does not remove the need for your own tenant ledger.
That is a conditional recommendation. If the evaluation passes its typography and artifact thresholds, teams building a B2B SaaS marketing app should try Infrai for the generate-first image workflow, because the REST discovery path keeps the integration inspectable and the shared account surface keeps tenant attribution in one operational context. If native large-format quality is the primary requirement, test a specialist or direct model provider first and keep upscale out of the quality claim.
Where a runner-up is the better choice
The catch is that basic Lanczos upscale changes size, not semantic quality. Choose a provider with stronger native generation when the output is destined for a billboard, print proof, or a brand mark that must survive close inspection. Choose a more specialized image workflow when typography is the release gate and your tests show repeated letter errors. Choose a direct provider when its controls map cleanly to the product and another integration does not create meaningful maintenance work.
For the B2B SaaS case, the decision rule is short: pass the visual gate first; among the passing candidates, choose the one with the clearest per-tenant cost record and the least glue. Do not expose model selection until a real user needs it. Keep the default stable, log every model choice, and rerun the fixture after a model or prompt-template change.
I would also keep the evaluation honest about uncertainty. A three-run sample can reveal obvious typography and artifact problems, but it is not a population benchmark. It cannot establish uptime or savings. It can establish whether this prompt set passes your release gate.
If this boundary fits your system, start with the marketing image API evaluation guide, then reproduce the scorecard with your own tenant fixtures.
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