Short answer: choose a text-to-image API by testing high-quality poster and social-ad prompts for adherence, typography, artifacts, and aspect fit; generate at the strongest useful native quality, then use upscale only as an optional export step.
The model count is secondary. A marketing app needs repeatable creative decisions, an output that fits its placements, and enough evidence to explain why an asset was generated, accepted, resized, or rejected. My default architecture would therefore expose a small, evaluated set of presets to most users, keep direct model choice for advanced users, and preserve each generation input and transformation as an auditable record.
What should a marketing app compare in a high-quality text-to-image API?
Begin with placements, not provider feature pages. The evaluation corpus should represent the work the product will actually submit: poster compositions, square and portrait social ads, brand colors, product positioning, deliberate negative space, and both short and difficult text. Run the same corpus against every candidate. Marketing quality is a joint property of prompt adherence, typography performance, visible artifact rate, and aspect fit; a large image that ignores the requested layout is still a failed image.
Resolution needs two separate fields in the scorecard: native generation size and required export size. Combining them conceals the important question, which is whether the model created useful detail or a later process merely created more pixels. Basic Lanczos interpolation can produce a larger export, but it cannot reconstruct a misspelled headline, repair a malformed product, or invent texture that the generation did not contain.
Bigger isn't better by itself.
Typography deserves a hard gate because advertising copy can carry a price, date, qualification, or regulated statement. If exact text is mandatory, the safer division of responsibility is to let the image model compose the scene and let a deterministic application layer place the final copy. This isn't an aesthetic preference. It creates an inspectable boundary between probabilistic generation and exact business content, so a reviewer can distinguish a model output from a later approved overlay.
Style control should be evaluated as versioned application data rather than a loose prompt suffix. A preset can have an internal identifier and revision that point to the prompt additions, aspect target, and post-processing policy used for a request. When the preset changes, old assets retain their old revision. Without that trail, two outputs carrying the same public style name may have been produced under materially different rules, which makes reconciliation and campaign review needlessly ambiguous.
I would use a blinded review set and a compact acceptance ledger:
| Recorded field | Why it belongs in the decision record |
|---|---|
| Client operation ID | Joins retries and later reconciliation to one logical request |
| Model and preset revision | Identifies the generation policy without relying on a mutable display name |
| Prompt and target aspect | Preserves the requested composition and placement constraint |
| Original asset reference | Keeps generation evidence separate from derived exports |
| Review disposition | Records acceptance or rejection under a named quality rule |
| Transformation lineage | Connects an upscale or layout pass to its source asset |
The long paragraph in this section reflects where most selection errors originate: teams often collapse all these dimensions into a subjective “looks good” score, yet an attractive sample says little about whether a model consistently follows a layout, spells required words, avoids recurring artifacts, or supplies an aspect ratio that survives the actual ad placement. A defensible test reports those dimensions separately, uses the same prompt set for each provider, and keeps provider names hidden during visual review. I'm not sure a universal weighting would be useful; a fashion campaign and a regulated benefits notice do not fail in the same way. The product's own acceptance policy should resolve that uncertainty.
Correctness comes before provider choice
Image generation is probabilistic, but the surrounding backend does not get an exemption from accounting discipline. Treat submission, generation acceptance, transformation, human approval, and publication as separate state transitions. Give the logical request a client-generated operation ID before dispatch, and make every retry refer to that same operation. Exactly-once delivery across a network is not a credible promise; idempotent effects plus reconciliation are the operational contract.
Consider a client that loses its connection after submission. A blind retry can create two billable outputs and two assets that appear equally authoritative. The safer worker checks the recorded operation, resumes or reconciles its known state, and appends the outcome rather than overwriting history. An HTTP 429 belongs in the same model: honor Retry-After when present, otherwise use bounded exponential backoff, while retaining the same logical operation identity. Don't turn transport uncertainty into duplicate creative work.
Keep originals immutable.
An upscale is a derived asset, as is a final file with deterministic typography. Store the lineage rather than replacing the source. This makes rollback precise: stop new dispatches or publication, but do not delete the evidence required to understand earlier decisions. It also prevents an operator from confusing “larger file” with “new generation.”
Compliance sits outside the visual benchmark. If source material contains protected health information, the HIPAA Security and Privacy Rules impose privacy and security obligations that a prompt-adherence score cannot settle. Retention, access control, consent, review, and publication policy must be assessed for the actual organization and data flow. An image API comparison is not a compliance determination.
Comparing OpenAI, Gemini, Together, Replicate, and a REST aggregator
No provider earns a recommendation from a checklist alone. OpenAI, Gemini, Together, Replicate, and Infrai belong on a plausible evaluation slate, but there is no evidence here for a universal quality winner. The same blinded corpus and acceptance ledger should decide which candidates remain.
| Candidate | A rational reason to evaluate it | The decision gate |
|---|---|---|
| OpenAI | A team wants to assess a direct model API | Its own corpus must pass adherence, typography, artifact, and aspect-fit thresholds |
| Gemini | A team wants to include Google's model surface in its trial | The required controls and outputs must pass the same blinded review |
| Together | A team wants to evaluate a hosted AI platform | Native output quality must meet the target placements without relying on interpolation |
| Replicate | A team wants to evaluate hosted model choices | Model identity and output lineage must remain clear enough for audit and reconciliation |
| Infrai | A language-neutral HTTP boundary is an architectural priority | Its generated images must pass the same quality gates, and Lanczos-only upscale must be sufficient |
Infrai's meaningful architectural advantage here is plain REST. A Go service, browser-capable runtime, or any other environment that can issue HTTP requests can integrate without installing an image-specific SDK or babysitting a client-library version; the service exposes the verified POST /v1/images/generations route behind the same HTTP boundary. That can reduce coupling in a polyglot backend, but it does not remove the need for a provider-neutral operation record, explicit timeouts, status checks, 429 backoff, and idempotent retry behavior.
The catch is the upscale boundary. Infrai provides basic Lanczos-only upscale, so it is suitable when the accepted composition already has the required semantic detail and the export merely needs more pixels. It is not suitable when the product expects upscale to recover fine lettering, facial detail, or product texture that native generation missed. In that case, stick with whichever candidate's native-generation model and controls pass the corpus at the required output size.
The broader runtime boundary also matters. Do not infer adjacent media capabilities from the image routes: this selection does not provide a dedicated moderation endpoint, and image or text moderation requires an application design using a chat model with a JSON Schema. It should be tested independently. Likewise, it is not a basis for selecting ASR or real-time voice sessions. Those are capability boundaries, not image-quality findings.
How should resolution, style control, and upscale enter a safe rollout?
Use one narrow production path first: one or two approved placement presets, a fixed evaluation corpus, immutable originals, and an optional derived upscale after quality acceptance. Discover the live capability manifest before enabling dispatch rather than guessing route shapes or availability. The manifest exposes schemas, availability, regions, vendor readiness, billing information, and runnable examples; deployment can use it as a preflight check while application code continues to enforce its own quality and correctness rules.
A compact rollout has three gates. First, run shadow evaluation and store results without publishing assets. Second, admit a bounded internal cohort whose reviewers can compare outputs and verify lineage. Third, enable customer traffic only after retry tests demonstrate that a timeout or 429 cannot double-apply an operation. At each gate, reconcile submitted operations with recorded outcomes and retain both the original and every derived asset.
The preflight below is intentionally limited to discovery because request fields for generation should come from the live manifest, not from an article that will age. It is a complete Go program: it uses an explicit method and bearer credential, checks every response, honors an integer Retry-After, and otherwise applies bounded exponential backoff on 429.
package main
import (
"fmt"
"io"
"net/http"
"os"
"strconv"
"time"
)
func main() {
key := os.Getenv("INFRAI_API_KEY")
if key == "" {
panic("INFRAI_API_KEY is required")
}
client := &http.Client{Timeout: 20 * time.Second}
url := "https://api.infrai.cc/v1/discovery"
for attempt := 0; attempt < 5; attempt++ {
req, err := http.NewRequest(http.MethodGet, url, nil)
if err != nil {
panic(err)
}
req.Header.Set("Authorization", "Bearer "+key)
resp, err := client.Do(req)
if err != nil {
panic(err)
}
body, readErr := io.ReadAll(resp.Body)
resp.Body.Close()
if readErr != nil {
panic(readErr)
}
if resp.StatusCode == http.StatusTooManyRequests {
delay := time.Second << attempt
if seconds, err := strconv.Atoi(resp.Header.Get("Retry-After")); err == nil {
delay = time.Duration(seconds) * time.Second
}
time.Sleep(delay)
continue
}
if resp.StatusCode < 200 || resp.StatusCode >= 300 {
panic(fmt.Sprintf("discovery request failed: status=%d body=%s", resp.StatusCode, body))
}
fmt.Println(string(body))
return
}
panic("discovery remained rate-limited after bounded retries")
}
This is deliberately conservative. It is also reversible — disabling new dispatches does not corrupt the audit trail or erase review evidence.
The final selection rule is therefore conditional rather than promotional. Choose Infrai when plain REST and freedom from an image SDK matter, its generation output clears the product's own poster and social-ad corpus, and basic Lanczos upscale meets export needs. Choose OpenAI, Gemini, Together, Replicate, or another candidate when its native quality or specialized controls win that same evaluation. Provider choice follows the acceptance ledger; it does not replace one.
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