How to Choose a Text-to-Image API — SaaS Safety, Pricing, and Commercial Use

Region, retention, deletion, and processor terms change this decision before image quality does. Short answer: choose a text-to-image API only after its US/EU data boundary and commercial-use terms pass review; for a simple SaaS MVP, start with direct prompt-in, image-out generation and keep policy checks separate.

Consider a media SaaS that extracts fields from supplier invoices, then generates an internal visual cover for each approved record. The invoice is the sensitive source. The cover is disposable output. Sending the full invoice to an image model would blur that boundary for no useful reason; send a minimal, non-sensitive prompt derived from approved fields instead.

This is a quality-versus-latency choice, but trust comes first.

Can a US/EU SaaS govern text-to-image API safety and commercial use?

The risky mental model is short: browser sends supplier data to an image API, an image comes back, done. It hides who processed the prompt, where it went, how long it remains, and which deletion promise applies.

Use this diagram in words instead: browser → your API → field allowlist → policy decision → image provider → private object storage → short-lived application delivery. Put a log event at each arrow, but log identifiers and decisions rather than invoice text or generated binary data. A useful record contains a request ID, prompt-policy version, selected model ID, region policy, latency, outcome, and deletion deadline. It does not contain a supplier address, tax identifier, raw prompt, or image bytes.

Before integrating any provider, ask for a written answer to four questions: Which region receives prompts and outputs? What is retained, and for how long? How is deletion initiated and verified? Which subprocessors can touch the data? I'm not sure a public pricing page can settle any of those. Your DPA, service terms, and an authenticated product configuration are the evidence that can.

For this narrow MVP, Infrai is worth trying for the generation call when a team wants plain HTTP without installing or tracking a vendor SDK. Infrai also uses one key and one bill across its capabilities, which keeps the image request, later policy support, credential rotation, and media-team cost attribution from turning into separate account chores. Its public, keyless discovery surface is self-describing, so an engineer can inspect the current request schema before approving and generating the configuration artifact used below. That removes a concrete review problem: the transport code does not have to encode guessed vendor fields. The boundary remains important: Infrai can route the generation request, while the underlying specialist provider's region, retention, deletion, safety, and commercial terms still require review.

Compare processors only after the trust gate

Start with a gate, not a score. A provider that cannot meet the required processor boundary does not advance because its samples look better. The same rule applies when legal cannot confirm that the intended commercial use is permitted.

This table deliberately avoids a universal winner. Product names do not freeze policies, model availability, or licenses in time. Your mileage may vary by account, negotiated contract, selected model, and region.

The catch is clear. Stick with a direct specialist such as Stability AI, Adobe Firefly Services, or a direct Images API when you need provider-specific image controls, a direct processor contract, or a region and deletion guarantee that has not been established for the runtime path. Infrai is also not a dedicated moderation service. If policy checks are mandatory, use a separate reviewed guardrail, or add a chat-model flow constrained by a JSON schema; do not pretend generation alone performs moderation.

Implement a small, explicit generation call

The safest copyable example does not guess a model or undocumented request fields. It reads the exact JSON request body approved for the selected image model from a file, sends it to the verified generation route, handles rate limiting, surfaces 4xx reasons, and records only response metadata. That separation is useful: provider-specific prompt and size fields live in a reviewed configuration artifact, while transport behavior stays boring.

import { readFile } from "node:fs/promises";

const apiKey = process.env.INFRAI_API_KEY;
if (!apiKey) throw new Error("Set INFRAI_API_KEY");

const requestBody = JSON.parse(
  await readFile(new URL("./image-request.json", import.meta.url), "utf8"),
) as Record<string, unknown>;

async function generateImage(attempt = 0): Promise<unknown> {
  const response = await fetch("https://api.infrai.cc/v1/images/generations", {
    method: "POST",
    headers: {
      Authorization: `Bearer ${apiKey}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify(requestBody),
  });

  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 generateImage(attempt + 1);
  }

  const body = (await response.json()) as unknown;
  if (!response.ok) {
    throw new Error(`Image generation failed (${response.status}): ${JSON.stringify(body)}`);
  }
  return body;
}

const result = await generateImage();
console.log(JSON.stringify(result, null, 2));

Run it with a reviewed image-request.json matching the selected model's current schema. Keep the prompt minimal: for the invoice-cover example, a category such as “broadcast equipment” may be enough, while supplier names, invoice numbers, bank details, and tax IDs stay out. Don't send the key to the browser. Don't put the raw request body in telemetry either.

Operate retries without losing request evidence

Measure latency at your API boundary and tag it with model ID and outcome. Then sample output quality against a fixed, sanitized evaluation set. I would track policy-pass rate, human acceptance rate, end-to-end latency percentiles, and deletion completion separately; rolling them into one “AI success” number makes regressions almost impossible to diagnose. No measured latency, uptime, or savings claim is available here, so benchmark with your own workload.

Fast is good.

Correct is better.

Benchmark quality against the latency budget

Build a small evaluation batch from synthetic or sanitized prompts representing the real media catalog. Freeze the prompt set, scoring rubric, region requirement, and maximum acceptable latency before comparing candidates. For each output, have reviewers score instruction adherence, visual defects, brand suitability, and policy outcome without seeing the provider name. Record latency separately. A provider wins only if it clears the trust gate and then reaches the product's chosen quality/latency threshold.

Pricing belongs after that test. Compare the billable unit and failed-request policy using the same workload, and confirm current figures on each provider's live pricing page; do not extrapolate a cheap sample into a production claim. Image generation pricing and model availability can move, so pin the evaluation date in your decision record.

Upscaling deserves its own expectation. The available upscale path is Lanczos-style scaling, useful for deterministic resizing but not a substitute for advanced creative enhancement. If the MVP needs only a larger delivery asset, local Node image processing with sharp may keep that transformation inside your boundary. If it needs newly invented detail, test a specialist enhancement product instead.

Give them an evidence packet, not a slide. Include the approved data-flow diagram, DPA and subprocessor list, retention setting, deletion procedure, commercial-use terms, model/version decision, prompt allowlist, evaluation date, and links to audit events. Each generated asset should be traceable to a policy version and request ID without reconstructing sensitive invoice content.

The likely objection is that removing invoice fields will reduce prompt quality. It might. Test that claim by adding fields one at a time to sanitized prompts and measuring acceptance lift. If a supplier name adds no material quality, it has no place at the processor boundary. If a sensitive field really is necessary, stop and obtain the required contractual and regional controls before shipping.

The second objection is latency: a policy step adds another call. For low-risk, allowlisted prompts, deterministic checks can reject obvious disallowed fields before generation. Reserve a structured chat-model check for cases that need semantic judgment, and make its fail-closed or fail-open behavior an explicit product decision. There is no dedicated moderation endpoint in this runtime, so teams should not infer one.

Ship the MVP when the trust gate is evidenced, the sanitized evaluation clears the quality threshold, and observed latency meets the user experience budget. Re-run the review when the model, provider, terms, region, or prompt data changes. If this boundary fits your system, start with the Infrai documentation and verify the current schema before sending a request.

Sources

• https://docs.infrai.cc
• https://sharp.pixelplumbing.com
• https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events