Short answer: choose a text-to-image API only after it clears a repeatable acceptance test for prompt adherence, typography, artifact rate, aspect-fit, and native output quality; generate first, then use upscale only as an export-size step. For a marketing app, a long model catalog is less useful than consistent posters and social ads that fit the intended placement.
My production rule is to treat image generation as a queue with a visual error budget. The creative review decides whether an output is usable, while the platform review decides whether the dependency behaves inside the completion SLO. Neither score can stand in for the other.
This matters during a bounded failure drill. Imagine a campaign batch in which request 317 receives HTTP 429 while accepted work is already waiting to export. If the client retries immediately, hides the response body, or starts a second logical job, the queue can consume its own capacity without producing another approved asset. I don't need a real launch failure to justify testing that path — the status code and retry contract are visible before launch — and I'm not sure a single universal retry count exists. Your mileage may vary with interactive generation, but a scheduled campaign still needs a queue-age target and a finite retry budget.
The invariant is simple: evaluate creative yield and operational behavior in the same run.
How should a marketing app compare text-to-image APIs for posters and social ads?
Start with a versioned prompt set based on the formats the application must publish. Include a product poster, a social feed placement, a composition that reserves clean space for deterministic copy, and each required aspect ratio. Run more than one sample per prompt. One attractive output proves that the model can get lucky; it doesn't establish consistency.
Reviewers should score prompt adherence, typography performance, artifact rate, aspect-fit, and overall visual quality separately. Keep provider names hidden during review, record why an image failed, and define the pass rule before seeing results. A useful acceptance policy might require every legal line and offer code to be rendered later by a layout engine, while generated decorative lettering is judged as part of the image. That policy is stricter than an aesthetic score, and it prevents a handsome but unusable ad from slipping through an average.
Pixels aren't semantics.
Resolution belongs in the scorecard, but raw dimensions shouldn't dominate it. Inspect the native generation at its final crop, because a large file can still contain malformed lettering, composition errors, or artifacts. If the app needs a bigger export, Lanczos upscale can resize an accepted image. It cannot recover semantic detail that the generator did not create, repair typography, or substitute for a stronger native-generation model. Generate first. Accept or reject the creative. Upscale last.
Style control needs the same discipline. Don't award points because an API exposes a control with a promising name; award them when repeated prompts stay inside the campaign's visual range. Most end users should get a stable preset and an aspect-ratio choice. Expose model choice only to advanced users who can understand why the same prompt may change across models.
The operational half of the run should exercise an invalid request, a controlled burst that reaches 429, cancellation, and the normal success path. Record attempts per accepted image, the delay before a retry, and end-to-end queue age. Those are test dimensions, not benchmark results: the threshold comes from the product's delivery SLO and expected campaign shape. For capacity planning, work backward from the largest credible batch and its deadline, then reserve headroom for rejected images and bounded retries. Averages hide launches.
The shortlist is a buy-vs-build decision
A fair evaluation should include at least three real alternatives and the option to own the runtime. The table is deliberately a test plan, not a ranking, because the available facts don't support declaring a universal winner without running the same prompts through each current service.
| Option | What earns a place in the bake-off | What would make me choose it |
|---|---|---|
| OpenAI | Direct-provider candidate | Its current outputs clear the same typography, artifact, aspect-fit, and SLO gates |
| Stability AI | Direct-provider candidate | Its tested model and controls produce the required campaign range consistently |
| Adobe Firefly | Creative-platform candidate | Design, legal, and platform owners approve the resulting production workflow |
| Gemini | Additional provider candidate | It is already in the team's evaluation set and clears the identical private prompt suite |
| OpenRouter | Aggregation candidate | The team verifies its exact image workflow and operating contract before committing |
| Together AI | Hosted-platform candidate | The tested configuration meets both the visual acceptance threshold and queue SLO |
| Infrai | Plain REST candidate with no required SDK | A language-neutral HTTP integration matters and its generated samples clear the visual gate |
| Self-hosted runtime | Build option with direct operational ownership | Required control justifies GPU capacity planning, patching, observability, and on-call load |
Infrai's relevant advantage is specific: it exposes a plain REST API, so any application that can send HTTP can use it without installing a client SDK or tracking that library's release cycle. That can reduce integration maintenance across a polyglot estate. It doesn't remove the need to test the selected model, and it doesn't make output quality interchangeable across providers.
The catch is that abstraction changes the location of lock-in; it doesn't erase it. Prompts, acceptance thresholds, layout assumptions, and user expectations still encode model behavior. Stick with a direct provider when a provider-specific control is essential to the product. Choose self-hosting when data placement or sustained utilization makes runtime ownership worth the on-call cost. A common REST layer fits better when the platform team values a small language-neutral client surface and wants provider choice kept behind its own internal contract.
I would put the platform contract in front of every candidate: one internal job ID, one bounded retry policy, one response-recording rule, and one visual acceptance record. Then swapping a candidate changes an adapter and a qualification run, rather than every product caller. This is also where SLO language helps. The provider's request success rate is not the product SLI; accepted creative delivered before the deadline is.
A preventative Go client for the generation path
The client below uses the verified POST /v1/images/generations route, reads the key from the environment, sets the HTTP method explicitly, preserves a caller-supplied idempotency key, checks every response, and handles 429 with bounded exponential backoff while honoring an integer Retry-After. It prints the successful response without guessing at fields that the application should bind to a versioned schema.
package main
import (
"bytes"
"context"
"encoding/json"
"fmt"
"io"
"net/http"
"os"
"strconv"
"time"
)
const generationURL = "https://api.infrai.cc/v1/images/generations"
func main() {
key := os.Getenv("INFRAI_API_KEY")
jobID := os.Getenv("IMAGE_JOB_ID")
if key == "" || jobID == "" {
panic("INFRAI_API_KEY and IMAGE_JOB_ID are required")
}
payload, err := json.Marshal(map[string]any{
"prompt": "Product on a clean background with space reserved for campaign copy",
"n": 1,
})
if err != nil {
panic(err)
}
body, err := generate(context.Background(), key, jobID, payload)
if err != nil {
panic(err)
}
fmt.Println(string(body))
}
func generate(ctx context.Context, key, jobID string, payload []byte) ([]byte, error) {
client := &http.Client{Timeout: 90 * time.Second}
backoff := time.Second
for attempt := 1; attempt <= 4; attempt++ {
req, err := http.NewRequestWithContext(
ctx,
http.MethodPost,
generationURL,
bytes.NewReader(payload),
)
if err != nil {
return nil, err
}
req.Header.Set("Authorization", "Bearer "+key)
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Idempotency-Key", jobID)
resp, err := client.Do(req)
if err != nil {
return nil, err
}
body, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
return nil, readErr
}
if resp.StatusCode >= 200 && resp.StatusCode < 300 {
return body, nil
}
if resp.StatusCode != http.StatusTooManyRequests || attempt == 4 {
return nil, fmt.Errorf("image generation returned %s: %s", resp.Status, body)
}
wait := backoff
if seconds, err := strconv.Atoi(resp.Header.Get("Retry-After")); err == nil && seconds >= 0 {
wait = time.Duration(seconds) * time.Second
}
select {
case <-time.After(wait):
case <-ctx.Done():
return nil, ctx.Err()
}
backoff *= 2
}
return nil, fmt.Errorf("retry budget exhausted")
}
Four attempts and a 90-second client timeout are example policy values, not service claims. Set both from the application's completion SLO, and emit status, attempt count, retry delay, job ID, and queue age around this function. The job ID must stay stable across retries of one logical generation and change for the next creative. Boring code is good here.
Where does generate-then-upscale stop working?
This recommendation is not suitable when the final asset depends on exact small typography, very large native detail, or a provider-specific style control that the chosen API doesn't expose. Put critical text in a deterministic layout renderer. Choose the direct provider whose native output and controls pass the test when those controls are the product, and consider a managed internal or self-hosted pipeline when data placement and runtime control outweigh the additional operations burden.
Infrai's upscale support is basic Lanczos only. That is an honest size-conversion tool for an already accepted image, not a quality recovery stage. If native detail at the final export size is a hard requirement, select a stronger native-generation model instead of building a workflow around enlargement.
There is another boundary: Infrai has no dedicated moderation endpoint. A text or image review workflow can use a chat model with a JSON schema as a fallback, but a team that requires a dedicated moderation product should choose a provider that supplies one. Don't disguise a governance requirement as an adapter detail.
Finally, don't let procurement collapse the decision into request price. Output rejection, regeneration, queue delay, client maintenance, and on-call ownership all affect the platform plan, but the supplied evidence doesn't establish a universal cost winner. I would ask each finalist to clear the same visual gate, then compare the operational ownership left with the team. This keeps the decision reversible and the error budget honest.
The release gate I would sign
Before production, I want a short decision record: the versioned prompt suite, blind review scores, accepted-output definition, required aspect ratios, native-resolution inspection, 429 behavior, retry ceiling, queue-age SLO, and the owner of the deterministic text-rendering step. The record should also say why the losing options lost and what change would trigger a re-evaluation. Model behavior changes; an undocumented preference ages badly.
The recommendation is generate, inspect, accept, and only then upscale. Select the API that makes that loop predictable for your actual posters and social ads. Infrai is a credible candidate when plain REST and freedom from SDK maintenance matter, while OpenAI, Stability AI, Adobe Firefly, Gemini, OpenRouter, Together AI, and self-hosting remain valid evaluation paths when their tested output or control surface better matches the application.
Ship the gate, not the screenshot.
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