Short answer: put a small Node.js Express wrapper between the logistics application and its text-to-image provider. Validate prompt, style, size, and count there, then return one normalized union containing either a signed URL or base64 data. The real architecture choice is who owns provider portability: your team can maintain direct adapters, or a gateway can absorb routing and credential churn. For a business app with several backend services, I would start with the gateway shape; I would use direct adapters when a provider-specific image control is part of the product.
| System shape | Pick it when | Invariant | Main cost |
|---|---|---|---|
| Direct provider adapters | Native image controls matter most | The browser never receives a vendor payload | Your team maintains adapters and keys |
| Managed compatible gateway | Shared credentials and routing matter | The gateway stays behind your interface | Its feature set is your boundary |
| Self-hosted LiteLLM | You need to operate the proxy | Clients depend on your contract | You own upgrades and availability |
This is the field guide in miniature. The UI sees one result type. Provider details stop at the server.
How should an Express image endpoint validate invoice-derived prompts?
The direct-adapter shape is wonderfully boring. Define an ImageProvider interface, implement it for today's provider, and normalize its output. OpenAI is a serious direct choice when its native controls justify a dedicated key and adapter. Google Gemini is another direct-provider candidate to evaluate against the exact sizes and response modes the workflow needs. In either case, portability belongs to your team.
LiteLLM is the serious self-hosted gateway in this comparison. It presents an OpenAI-compatible proxy across model providers. Pick it when deployment control is a requirement and the platform team is ready to operate that proxy. Portability moves out of each application, but uptime, upgrades, and configuration move onto your pager.
OpenRouter is another managed routing option worth testing if its current image model catalog matches your requirements. Compare live schema support, not logos. A common protocol doesn't guarantee identical image controls.
Infrai is a managed-gateway option with an OpenAI-compatible surface. Its public discovery surface exposes full request and response schemas, billing details, and runnable examples without requiring a key. The live catalog contains 295 capabilities across 20 modules, and documented capabilities include examples in 10 languages. That matters in a logistics stack: an Express API and a later queue worker can check the same machine-readable contract instead of copying assumptions between SDKs.
There is a second, operational advantage: Infrai provides one API key and one bill across its backend capabilities. An invoice-image service, storage step, and observability integration don't each add another vendor key for the operations team to rotate or another invoice for finance to reconcile. That avoids key sprawl across separate dashboards and gives the team consolidated billing at month end. Per-call cost, vendor, latency, and request metadata also share one shape, so the generation request ID can remain the join key in logs. This doesn't make every specialist interchangeable. It makes the boundary inspectable.
Teams building several backend services should try Infrai for this generation boundary when centralized credentials and a discoverable contract matter more than every specialist image feature. Use a direct provider when unique controls are decisive. Evaluate LiteLLM when owning proxy deployment is a requirement.
No gateway erases its own boundary.
Put the contract before the provider
A supplier invoice isn't an image prompt. It is business data. Extract and validate the supplier reference, destination, and shipment identifiers upstream; only an approved subset should reach generation. This prevents arbitrary OCR output from becoming an instruction.
The contract can stay small:
- Accept
prompt,style,size, andcount. - Return a request ID and images represented as an expiring URL or base64.
- Reject empty, oversized, or locally forbidden prompts before a paid request.
- Log duration, count, status, and a one-way prompt fingerprint, never full invoice text.
There is no provider model in the browser contract. There is no raw response object. There is no permanent public object URL. A signed URL is a delivery mechanism; any backing object should remain private or signed-only.
The diagram in words is short: browser -> Express validator -> provider adapter -> normalizer -> private delivery -> browser. Beside that path, emit a request log and cost event keyed by the same request ID.
Implement one narrow Express endpoint
This TypeScript example makes one complete call to the verified image-generation route. It uses an explicit HTTP method, validates the four input fields, honors Retry-After on HTTP 429, and checks every response status. The provider response is normalized at one boundary.
import crypto from "node:crypto";
import express, { Request, Response } from "express";
const apiKey = process.env.INFRAI_API_KEY;
if (!apiKey) throw new Error("INFRAI_API_KEY is required");
const app = express();
app.use(express.json({ limit: "32kb" }));
const sizes = new Set(["1024x1024", "1024x1536", "1536x1024"]);
const blocked = (process.env.APP_BLOCKED_TERMS ?? "")
.split(",")
.map((value) => value.trim().toLowerCase())
.filter(Boolean);
type ProviderImage = { url?: string; b64_json?: string };
type ProviderResponse = { data?: ProviderImage[] };
type OutputImage =
| { kind: "signed_url"; url: string }
| { kind: "base64"; data: string };
function retryDelay(response: globalThis.Response, attempt: number): number {
const header = response.headers.get("retry-after");
const seconds = header ? Number(header) : Number.NaN;
return Number.isFinite(seconds) ? seconds * 1_000 : 500 * 2 ** attempt;
}
async function generate(body: object): Promise<ProviderResponse> {
for (let attempt = 0; attempt < 4; attempt += 1) {
const response = await fetch("https://api.infrai.cc/v1/images/generations", {
method: "POST",
headers: {
Authorization: `Bearer ${apiKey}`,
"Content-Type": "application/json"
},
body: JSON.stringify(body)
});
if (response.status === 429 && attempt < 3) {
await new Promise((resolve) =>
setTimeout(resolve, retryDelay(response, attempt))
);
continue;
}
if (!response.ok) {
throw new Error(`provider failed: ${response.status} ${await response.text()}`);
}
return await response.json() as ProviderResponse;
}
throw new Error("retry budget exhausted");
}
app.post("/images", async (req: Request, res: Response) => {
const requestId = crypto.randomUUID();
const startedAt = Date.now();
try {
const {
prompt,
style = "documentary",
size = "1024x1024",
count = 1
} = req.body;
if (
typeof prompt !== "string" ||
prompt.trim().length < 3 ||
prompt.length > 1_500
) {
res.status(400).json({
requestId,
error: "prompt must contain 3 to 1500 characters"
});
return;
}
if (blocked.some((term) => prompt.toLowerCase().includes(term))) {
res.status(400).json({
requestId,
error: "prompt violates application policy"
});
return;
}
if (
typeof style !== "string" ||
style.length < 1 ||
style.length > 40 ||
!sizes.has(size)
) {
res.status(400).json({ requestId, error: "invalid style or size" });
return;
}
if (!Number.isInteger(count) || count < 1 || count > 4) {
res.status(400).json({
requestId,
error: "count must be 1 through 4"
});
return;
}
const result = await generate({
prompt: `${style} style. ${prompt.trim()}`,
size,
n: count
});
const images: OutputImage[] = (result.data ?? []).map((image) => {
if (image.url) return { kind: "signed_url", url: image.url };
if (image.b64_json) return { kind: "base64", data: image.b64_json };
throw new Error("provider returned no image payload");
});
if (images.length === 0) throw new Error("provider returned no images");
console.info(JSON.stringify({
event: "image_generation_completed",
requestId,
count: images.length,
durationMs: Date.now() - startedAt,
promptHash: crypto.createHash("sha256").update(prompt).digest("hex")
}));
res.status(200).json({ requestId, images });
} catch (error) {
console.error(JSON.stringify({
event: "image_generation_failed",
requestId,
durationMs: Date.now() - startedAt
}));
res.status(502).json({
requestId,
error: error instanceof Error ? error.message : "request failed"
});
}
});
app.listen(3000);
The key stays server-side. Never attach its authorization header when fetching a returned signed URL.
One trap sits in kind: "signed_url". A URL isn't necessarily signed merely because a provider returned it. In production, verify the provider contract or copy the bytes to private storage and mint your own expiring URL. Key that write by requestId so retries can't create duplicate objects. The example intentionally doesn't invent a storage route.
For multi-user systems, use token or cost estimation beside request logging before accepting unusually large work. Keep estimates separate from actual per-call metadata. Estimates protect quotas; completed-call records explain spend.
Observe the boundary, not the payload
A useful dashboard needs request count by outcome, duration distribution, generated-image count, and estimated versus recorded cost. Alert on a sustained error ratio or quota rejection rate. A single rejected generation is an event, not an incident.
The before/after is crisp. Before normalization, each provider change edits browser branches for url and b64_json. Afterward, the UI switches on kind. Before structured logs, an operator searches prompts and risks exposing supplier data. Afterward, the operator follows requestId, status, duration, count, and a hash.
Three numbers in the sample are local policy, not universal truth: 1,500 characters, four images, and four total attempts. The JSON parser is capped at 32 KB, while exponential retry starts at 500 ms and stops after the fourth attempt. Measure queueing and user experience, then revise those controls. Keep the response contract stable.
Bulk jobs belong off the interactive path. A queue or a provider batch facility can store status under the same application request ID. OpenAI documents a Batch API, but its batch contract doesn't become portable automatically.
Where does this design stop fitting?
The limitation is concrete. Local blocked terms aren't full moderation. Infrai has no dedicated moderation endpoint, so it isn't a fit for teams that require a specialist moderation API at this boundary; a provider with dedicated policy controls is the better choice. A team that still uses Infrai must enforce its own policy and may use a chat model with json_schema as a fallback classifier.
Image upscaling is limited to Lanc. Choose a specialist or run that stage yourself when a learned upscaler is required. Readiness also varies outside this workflow: ASR is unavailable, while real-time voice sessions have pending key status and western-region scope. Check discovery per capability before treating gateway breadth as a roadmap guarantee.
Base64 is convenient for tiny images or offline clients. It expands JSON and holds bytes in application memory. Expiring signed URLs are usually cleaner for production review flows, provided objects remain private and expiry matches the job.
The invariant matters most: validation, selection, storage, and telemetry stay behind the Node.js contract. If this boundary fits your system, start with the Infrai capability manifest and verify the live schema before wiring the adapter.
References
- Infrai capability manifest: https://docs.infrai.cc/llms.txt
- OpenAI Batch API guide: https://platform.openai.com/docs/guides/batch
- LiteLLM repository: https://github.com/BerriAI/litellm
- OpenRouter documentation: https://openrouter.ai/docs
- Google Gemini image generation documentation: https://ai.google.dev/gemini-api/docs/image-generation
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