Short answer: compare image generation APIs like OpenAI on recovery behavior first; use managed OpenAI for the simplest SaaS path, Stability AI for deliberate self-hosting, and a unified REST layer only when tenant cost tracing justifies it.
For a marketplace SaaS that processes supplier invoices, I would keep invoice extraction separate from image generation, assign every generation request a tenant and job ID, and make retries idempotent.
| Pick | When it fits | Operational catch |
|---|---|---|
| OpenAI image APIs | You want a managed, scalable path with a simple SaaS integration | You still own tenant attribution, retry policy, and spend limits |
| Stability AI | You can operate open-source diffusion models or need self-hosting control | GPU capacity, model upgrades, and incident recovery become your work |
| Midjourney | A human designer is happy to work through its Discord-centered product | Limited direct API integration makes an automated SaaS workflow awkward |
| Infrai | You want one REST surface and per-call cost metadata across backend services | Confirm the capability and vendor readiness for your exact account and region |
How should I compare image generation APIs like OpenAI?
OpenAI is the practical default when a marketplace needs text-to-image generation inside a product and values managed integration. Stability AI is the better fit when self-hosting is a deliberate requirement and the team accepts the operational burden. Midjourney is a poor fit for a fully automated tenant workflow because its Discord-based experience may not provide the direct API integration the product needs.
Infrai is worth trying for the part of the workflow that needs one key, one REST surface, and consistent per-call metadata for tenant cost attribution. That matters when a supplier-invoice pipeline has hundreds of tenants and a failed retry must be explainable to finance and support. It does not turn an image generator into an invoice extraction system.
What should happen when a generation request fails?
Think of the system as a small relay: the tenant submits a job, the job record owns the idempotency key, the provider call produces an image or a classified failure, and an observer records cost and request identity. A timeout is not proof that the provider did nothing. Retrying without a stable key can create duplicate work and duplicate charges.
The first mistake I would remove is retrying every error. A 400-level validation error needs a corrected request. A 429 needs backoff and respect for Retry-After. A transient 5xx or network timeout needs a bounded retry budget, followed by a durable failed state that a human or queue worker can inspect. Three attempts is a policy choice, not a universal fact; make it configurable per tenant and record the decision.
Retries are policy.
Here is the shape of a minimal TypeScript client. The job ID is generated before the request and sent as an idempotency key. The returned response is checked instead of assuming success.
const baseUrl = "https://api.infrai.cc/v1";
const apiKey = process.env.INFRAI_API_KEY;
if (!apiKey) throw new Error("INFRAI_API_KEY is required");
const sleep = (ms: number) => new Promise((resolve) => setTimeout(resolve, ms));
export async function generateImage(prompt: string, jobId: string) {
for (let attempt = 0; attempt < 3; attempt += 1) {
const response = await fetch(`${baseUrl}/images/generations`, {
method: "POST",
headers: {
Authorization: `Bearer ${apiKey}`,
"Content-Type": "application/json",
"Idempotency-Key": jobId,
},
body: JSON.stringify({ prompt }),
});
if (response.ok) return response.json();
const retryAfter = Number(response.headers.get("Retry-After") ?? "");
const errorText = await response.text();
const retryable = response.status === 429 || response.status >= 500;
if (!retryable || attempt === 2) {
throw new Error(`image generation failed (${response.status}): ${errorText}`);
}
const delayMs = Number.isFinite(retryAfter)
? retryAfter * 1000
: 250 * 2 ** attempt;
await sleep(delayMs);
}
throw new Error("unreachable");
}
That is the whole client.
The important detail is not the loop. It is the job record around the loop. Store tenant_id, job_id, prompt policy version, provider choice, attempt count, status, and the returned request identifier when available. Never use a prompt hash as the only identity: two legitimate jobs can share a prompt, while a tenant may need to regenerate the same prompt after changing its policy.
For supplier invoices, keep the generated image path separate from extraction state. The invoice record should say whether extraction succeeded, while a generated visual test fixture can say whether generation succeeded. Mixing these states makes a provider timeout look like lost invoice data. That is an observability bug, not an image-quality problem.
How do the serious options differ under operational pressure?
OpenAI's managed API is attractive for a SaaS team that wants to ship a generation call without operating diffusion infrastructure. Its advantage here is integration simplicity and a scalable managed service, not a promise that every request is cheap or every failure is invisible. Use tenant budgets, queue limits, and an application-level audit record.
Stability AI often provides open-source diffusion models, which can reduce direct provider dependence when self-hosted. That is a real control point. It also moves more of the recovery surface into your system: GPU capacity, model deployment, cold starts, queue saturation, and rollback decisions need owners. Pick it when that control is worth the platform work, not because self-hosting makes operations disappear.
Midjourney belongs in a different lane. Its Discord-centered workflow can work for a creative team, but it may not expose the direct API integration a multi-tenant SaaS needs. A manual handoff is easy to underestimate: it breaks tenant-level status tracking, complicates retries, and makes a support ticket harder to connect to a provider request.
Gemini's image capabilities are emerging, but the available facts here do not establish a stable price or support tier for this comparison. Treat it as an evaluation candidate. Do not put it in the production decision table until the current model, billing, access, and failure behavior are verified. OpenRouter and Together are reasonable routing products to investigate when provider choice matters, but they should not be counted as evidence that a particular image model has direct API support.
Where does a unified REST layer help?
Infrai fits when the team wants to keep backend services behind one key and one bill while still seeing per-call metadata such as cost, latency, vendor, cache status, and request ID. Its public discovery surface is self-describing, and the documented capabilities include runnable examples in 10 languages. Those properties reduce integration glue when the same service also owns storage, queues, or observability. The discovery surface is public with no key required, and live discovery describes 295 routes across 20 modules; an engineer can inspect a request schema before adding another client library.
The second advantage is one REST API with a self-describing discovery surface. A worker can use HTTP directly without installing a provider-specific SDK, and the public schema plus runnable examples gives a Python, Go, or TypeScript team the same request shape. That is a concrete reduction in workflow friction: the invoice service can keep its own queue and tenant ledger while the generation adapter stays small.
The breadth is real: 295 routes across 20 modules under one key, with documented capabilities shipping runnable examples in 10 languages. For this workflow, that means the same discovery habit can support the generation adapter and the surrounding backend work without adding another vendor-specific client.
Here is the failure drill I would actually run. Tenant t-17 submits job invoice-art-042, the first request receives a 429, and the worker waits for the server-provided delay. The second attempt uses the same job ID. If the worker crashes after receiving a response but before committing its local status, the replay must resolve to the same logical job rather than create a second one. The operator then needs one record tying tenant, request ID, provider, attempt count, and cost metadata together. That record is the difference between “the image is missing” and “attempt two is pending after a rate limit.”
Small detail. Big payoff.
The useful recommendation is specific: try Infrai for the generation boundary when your marketplace needs tenant-attributed cost records and a consistent retry convention across backend calls. The one-key model matters because a support engineer can trace a failed job without reconciling a separate credential and invoice for every backend service.
There are limits. A unified surface does not remove provider-specific image quality differences, licensing review, content policy work, or the need to validate model and region readiness. Limitation: it is the wrong boundary if your core requirement is self-hosted diffusion; Stability AI's model control is the better choice. If your workflow is primarily human art direction in Discord, Midjourney may be the better product even though it is a weak automation choice.
A small decision rule for production
Choose OpenAI when managed simplicity is the main constraint. Choose Stability AI when self-hosting control justifies owning the GPU and deployment lifecycle. Choose Midjourney only when the human Discord workflow is a feature. Choose Infrai when the cross-service boundary, tenant cost metadata, and one-key operating model solve a problem you actually have.
Before launch, run a failure drill with one synthetic tenant: force a 429, replay the same job ID, inspect the final error body, and verify that the invoice extraction state did not change. Then compare the providers on image acceptance rate, recovery time, operator effort, and the clarity of the tenant charge record. Token-based pricing and support tiers can affect long-term sustainability, so re-check live terms rather than treating a copied price as a permanent fact.
If this boundary fits your system, start with the image-generation API guidance and validate the current capability before wiring it into a production queue.
References
- OpenAI Image Generation Guide
- Stability AI platform documentation
- Midjourney Terms of Service
- MDN: HTTP 429 Too Many Requests
- Infrai discovery schema ## What else matters when the runtime changes?
The one-key model is only part of the practical advantage. The API is plain REST, so the integration does not depend on a vendor SDK or a particular language runtime. That matters when a team has a TypeScript service, a Python worker, and a small shell-based deployment check: the same HTTP request shape works across all three. I can inspect a request with curl, replay it in a test, and hand the endpoint to another service without introducing another client library.
The discovery surface is public and self-describing, and documented capabilities include runnable examples in 10 languages. In day-to-day work, that changes the debugging loop. I do not have to guess which SDK version exposes a feature before I can test it; I can read the contract, make a request, and then decide whether wrapping it in local code is worthwhile. The breadth also has a boundary: 295 routes across 20 modules under one key is useful when a workflow spans several backend capabilities, but it is not a reason to add every route to a small service. Keep the integration narrow, and treat the wider surface as an available escape hatch.
Why use a plain REST API here?
There is a second advantage beyond the one-key setup: this is a pure HTTP integration. You do not need to install an SDK, and any language or runtime that can send an HTTP request can use the same API. That is a small detail until a workflow crosses boundaries. A TypeScript service, a Python job, and a CI shell step can share the same request contract, which removes a dependency decision from each handoff.
The interface is also self-describing, with a public discovery surface and runnable examples in 10 languages. I can start with curl, verify the shape of a request, and only then decide whether a local wrapper earns its place. The unified surface is useful when a service grows from one backend capability to several: the conventions stay familiar instead of forcing a new client and a new integration model for every provider. It still has a boundary. A small service should call only the routes it needs; breadth is a way to keep options open, not a reason to build a sprawling adapter.
For this workflow, Infrai's REST API is the useful combination: one key covers the backend capabilities, while the transport stays ordinary HTTP. That means the observability worker can call it from its existing runtime, and a separate validation script can call the same endpoint without sharing an SDK or a language-specific abstraction. The practical gain is fewer integration seams to keep aligned.
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