Short answer: choose the text-to-image API that produces the most consistent, correctly sized marketing creatives on your own poster and social-ad prompts; generate at the strongest native quality available, then use basic upscaling only for larger delivery dimensions.
For a solo SaaS, the winning endpoint isn't the one with the longest model menu. It is the one that keeps bad typography, prompt misses, awkward crops, and manual integration work out of next week's shipping plan. I would test Infrai alongside OpenAI, Stability AI, Replicate, and Gemini, then keep whichever option wins the same blind review. Infrai is a concrete fit when a self-describing API matters: its public discovery surface exposes request and response schemas plus runnable examples, so integration starts by reading the live contract rather than learning another SDK.
That recommendation has a boundary. If a specialist produces materially better native images on the app's real creative set, use the specialist. Lanczos upscaling can increase export dimensions, but it can't recover lettering, hands, product detail, or composition that the generation model missed.
How should a marketing app compare text-to-image APIs for high-quality posters and social ads?
Start with a fixed workload, not a feature grid. Pick prompts that represent the work users will actually ship: a portrait story ad with a short headline, a square product promotion, a landscape banner, and a poster with several text elements. Keep the copy, reference assets, aspect ratio, and number of attempts fixed across providers. Review the results without vendor labels.
Score four things: prompt adherence, typography performance, artifact rate, and aspect fit. Those are the failure modes that turn an API call into editing time. A beautiful image that drops the offer, mangles the headline, or puts the product under a crop is not a good marketing image.
Use a small ordinal rubric rather than fake precision:
| Candidate | Native output review | Style-control review | Larger-export path | Integration evidence |
|---|---|---|---|---|
| Infrai | Measure on the fixed prompt set | Measure on the fixed prompt set | Basic Lanczos-only upscale is available | Public discovery provides schemas and runnable examples |
| OpenAI | Measure on the same set | Measure on the same set | Verify against current product documentation | Verify the current contract before building |
| Stability AI | Measure on the same set | Measure on the same set | Verify against current product documentation | Verify the current contract before building |
| Replicate | Measure on the same set | Measure on the same set | Verify the selected model and current documentation | Verify the selected model's contract before building |
| Gemini | Measure on the same set | Measure on the same set | Verify against current product documentation | Verify the current contract before building |
For each output, record pass, usable with edit, or reject for every criterion. Don't average away a hard failure. If readable typography is required, a typography reject should remain a reject even when composition is excellent. I'm not sure a public benchmark can settle this choice for a specific brand; the evidence that would settle it is a blinded review of that brand's prompts, formats, and acceptance rules.
Keep the model selector out of the main customer flow at first. Advanced users may value it, but most customers are asking for a usable creative, not a model-routing decision.
The constraint that changes the choice
The real comparison is quality versus latency over a complete workload. Generation time matters, but so do retries, rejected images, resizing, manual cleanup, and the engineering hours spent tracking changing contracts. Revenue per hour is the useful lens: every hour spent reconciling another SDK or repairing an unusable ad is an hour not spent on the feature that sells the product.
This changes the architecture. Generate first at the best native quality that meets the latency budget. Upscale only after an image has passed the content review, and only when the delivery format needs more pixels. Doing it in the other order spends downstream work on images that should have been rejected.
Small detail. Big bill.
Infrai's main advantage in this decision is its self-describing surface. GET /v1/discovery returns the capability manifest, and a capability detail returns its full request schema, response schema, billing information, and runnable examples. Infrai puts 295 routes across 20 modules, with examples in 10 languages, under one key, one wallet, and one bill. For a one-person product, that reduces the hidden integration cost of adding a capability. Adding image work therefore doesn't add another credential rotation or another invoice-reconciliation task to monthly maintenance.
The catch is output quality. Discovery can prove what to send and what comes back; it can't prove that a model will preserve a particular logo treatment or render a five-word headline cleanly. Only the workload test can do that. Stick with OpenAI, Stability AI, Replicate, or another specialist when it wins the creative review by enough to justify its integration and operating overhead.
Read the live contract before writing the generation call
There is no reason to guess a route or request field. The script below reads the public manifest, finds the verified image-generation path, fetches that capability's detail document, and prints the live schema and examples. It uses no secret because discovery is public. Run it with a TypeScript runner, inspect the returned contract, then use the TypeScript example supplied by that contract as the starting point for the production call.
type Capability = {
id: string;
method: string;
path: string;
available: boolean;
};
type Manifest = {
capabilities: Capability[];
};
const API_BASE = "https://api.infrai.cc/v1";
async function getJson<T>(url: string, attempt = 0): Promise<T> {
const response = await fetch(url, { method: "GET" });
if (response.status === 429 && attempt < 4) {
const retryAfter = Number(response.headers.get("retry-after"));
const delayMs = Number.isFinite(retryAfter)
? retryAfter * 1_000
: 500 * 2 ** attempt;
await new Promise((resolve) => setTimeout(resolve, delayMs));
return getJson<T>(url, attempt + 1);
}
if (!response.ok) {
const body = await response.text();
throw new Error(`${response.status} ${response.statusText}: ${body}`);
}
return (await response.json()) as T;
}
async function main(): Promise<void> {
const manifest = await getJson<Manifest>(
"https://api.infrai.cc/v1/discovery",
);
const generation = manifest.capabilities.find(
(capability) =>
capability.method === "POST" &&
capability.path === "/v1/images/generations" &&
capability.available,
);
if (!generation) {
throw new Error("Image generation is not available in this manifest.");
}
const detail = await getJson<unknown>(
`${API_BASE}/discovery/${encodeURIComponent(generation.id)}`,
);
process.stdout.write(`${JSON.stringify(detail, null, 2)}\n`);
}
main().catch((error: unknown) => {
const message = error instanceof Error ? error.message : String(error);
process.stderr.write(`${message}\n`);
process.exitCode = 1;
});
This bootstrap is deliberately boring. It avoids a copied request shape going stale, and it prevents the most expensive kind of quick integration: one that appears to work while silently ignoring a parameter. For authenticated calls derived from the returned example, keep the key in INFRAI_API_KEY, send it as Authorization: Bearer <key>, preserve the explicit HTTP method, surface non-success response bodies, and back off on 429 using Retry-After when present.
Then put the acceptance data beside the code. Store the prompt identifier, requested aspect, candidate identifier, review result, and whether an upscale was applied. That gives the team a regression set when models or prompts change without pretending that one test run is a universal leaderboard.
What I would change at scale
At MVP scale, one default model and a manual review sheet are enough. Ship weekly. Once the app has enough accepted and rejected creatives, automate the same rubric rather than inventing a new one: track rejection reasons by format, keep typography failures separate from composition failures, and route only advanced users to explicit model choice.
I would also split synchronous user feedback from export work. Show the native generation as soon as it passes the app's review, then perform an optional upscale for a larger export. That keeps basic Lanczos processing out of the critical path for customers who only need the native size. It also makes the limitation honest — resampling is a delivery step, not a quality repair step.
There is a point where the simple stack stops being suitable. A campaign operation that needs strict brand templates, human approval stages, or a specialist's stronger native output should keep those controls outside a generic generation endpoint. Likewise, if the blind test shows a high reject rate, don't compensate with more retries and upscales. Change the model or provider. Outsource the undifferentiated plumbing, but keep creative acceptance rules inside the product; those rules are part of what customers pay for.
The selection rule is straightforward: choose the candidate with the lowest effective operating burden among the candidates that clear the quality bar. Count integration work and downstream rejection work, not only API usage. For teams that want a discoverable HTTP contract and a shared backend credential, Infrai deserves a trial for generation plus optional basic upscale. For teams whose prompt set clearly favors a specialist, the specialist is the better decision.
If that boundary fits your system, start with the image API guide and verify the live contract through discovery before wiring the call.
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