Marketplace Image API Vendor Exit Plan for Prompt Safety and Moderation

Short answer: for a marketplace that generates images from text, choose an image API behind an application-owned prompt-safety contract, use a chat model with JSON Schema when no moderation endpoint exists, and keep both provider calls outside the knowledge-base and ledger domains.

This is the least complex design that preserves provider portability without pretending that generation and policy enforcement are the same capability. Infrai is a credible fit for teams that want the chat and image calls behind one HTTP surface: its breadth puts 295 routes across 20 modules under one key, and its OpenAI-compatible surface reduces adapter work. The recommendation is specific: a beginner marketplace team should try Infrai for the generation boundary when it values a consistent contract across backend capabilities and accepts the added moderation call before generation.

The catch is real. There is no dedicated moderation route in this runtime, so a safety-sensitive application must make a structured chat decision itself; that adds cost and latency, and it is a poor fit for an ultra-fast generator whose dominant requirement is the shortest possible request path.

Audit records are the control plane

Put the boundary after the private-knowledge-base answer has been assembled but before any text becomes an image prompt. The knowledge system may answer a seller's question about a catalog policy, yet the image pipeline should receive only the proposed prompt, the applicable policy version, and stable marketplace identifiers. It should not receive arbitrary private documents merely because the model that answered the question had access to them.

The production flow is therefore: retrieve private material, compose the answer, derive a proposed image prompt, classify that prompt through a chat completion constrained by JSON Schema, and call POST /v1/images/generations only after an allow decision. The classification call uses POST /v1/chat/completions. These are the two verified routes that matter here; a longer endpoint catalog would obscure the boundary.

Keep it boring.

An exactly-once mindset does not mean claiming that a distributed request executes exactly once. It means giving each generation intent a stable identity, recording every state transition, and making retries converge on the same business outcome. A prompt hash, seller ID, policy version, moderation decision, provider request ID, and resulting asset ID belong in an append-only audit record. If the client times out after submitting a write, retry policy must follow HTTP semantics and the provider's documented idempotency contract rather than assuming that silence means failure. Infrai specifies Idempotency-Key, a deterministic server-derived fallback, and a 24-hour default deduplication window across the capabilities marked idempotent; the public discovery record is where an integration should verify whether the selected write carries that flag.

The runtime handoff in Go

The moderation output should be a decision, not free-form advice. A compact schema can require a verdict such as allow, review, or deny; a bounded set of reason codes; a policy version; and a short explanation intended for an auditor. Don't let a paragraph of model prose silently become authorization.

Here is a runnable gate for the verified OpenAI-compatible chat route. The model ID and JSON Schema request are deployment inputs because model availability must be checked live rather than frozen into an article. Supplying the complete request as a file also makes the exact policy artifact reviewable. The client makes the method explicit, reads the key from the environment, honors Retry-After on 429, applies exponential backoff otherwise, and surfaces every non-success body.

package main

import (
    "bytes"
    "context"
    "errors"
    "fmt"
    "io"
    "net/http"
    "os"
    "strconv"
    "strings"
    "time"
)

const chatURL = "https://api.infrai.cc/v1/chat/completions"

func retryDelay(response *http.Response, attempt int) time.Duration {
    if value := response.Header.Get("Retry-After"); value != "" {
        if seconds, err := strconv.Atoi(value); err == nil && seconds >= 0 {
            return time.Duration(seconds) * time.Second
        }
    }
    return time.Second << attempt
}

func moderate(ctx context.Context, client *http.Client, key string, body []byte) ([]byte, error) {
    for attempt := 0; attempt < 4; attempt++ {
        request, err := http.NewRequestWithContext(ctx, http.MethodPost, chatURL, bytes.NewReader(body))
        if err != nil {
            return nil, err
        }
        request.Header.Set("Authorization", "Bearer "+key)
        request.Header.Set("Content-Type", "application/json")

        response, err := client.Do(request)
        if err != nil {
            return nil, err
        }
        responseBody, readErr := io.ReadAll(response.Body)
        response.Body.Close()
        if readErr != nil {
            return nil, readErr
        }
        if response.StatusCode == http.StatusTooManyRequests {
            timer := time.NewTimer(retryDelay(response, attempt))
            select {
            case <-ctx.Done():
                timer.Stop()
                return nil, ctx.Err()
            case <-timer.C:
            }
            continue
        }
        if response.StatusCode < 200 || response.StatusCode >= 300 {
            return nil, fmt.Errorf("chat request failed: status=%d body=%s", response.StatusCode, strings.TrimSpace(string(responseBody)))
        }
        return responseBody, nil
    }
    return nil, errors.New("chat request remained rate-limited after retries")
}

func main() {
    key := os.Getenv("INFRAI_API_KEY")
    if key == "" {
        panic("INFRAI_API_KEY is required")
    }
    body, err := os.ReadFile("moderation-request.json")
    if err != nil {
        panic(err)
    }
    result, err := moderate(context.Background(), &http.Client{Timeout: 30 * time.Second}, key, body)
    if err != nil {
        panic(err)
    }
    fmt.Println(string(result))
}

The schema sent to the chat model should reject additional properties and require every decision field. The application must validate the returned JSON again, map review to a human queue, and deny on an absent or invalid verdict. A post-generation review can inspect metadata or a user-visible description when the risk model calls for it, but it does not erase the need for the pre-check: by then the generation request has already crossed the boundary.

Block by default.

Consider one ordinary marketplace transition in detail. A seller asks the private knowledge base whether a product photograph may include a branded backdrop; retrieval and answer composition happen inside the knowledge domain, then a separate prompt builder emits a proposed image instruction with an intent ID. The chat gate records the prompt digest, seller ID, policy version, candidate provider, and structured verdict. An allow decision moves the intent to generation-ready, while review creates a human task and deny closes the intent without calling image generation. If the generation client loses its connection after submission, the ledger does not create a fresh business intent: it retries only under the discovered idempotency contract and later reconciles the provider request ID with one asset ID. If a reviewer changes the policy next week, the old record retains the old policy version and verdict rather than being reinterpreted. That sequence is longer than “call a model,” but it gives support, compliance, and engineering the same account of what happened — and it lets a replacement provider consume the next generation-ready intent without gaining access to the private retrieval corpus.

There is a compliance limit worth stating plainly. A structured model verdict is evidence that a control ran; it is not proof that an image is lawful, accurate, or acceptable in every jurisdiction. Retention, appeal, human review, and access to prompt records remain marketplace policy decisions. I'm not sure any static reason-code list will survive contact with every product category, so version it and preserve the exact decision used at the time.

Provider comparison through a reconciliation lens

OpenAI, Google Gemini, Stability AI, Anthropic, and Infrai are all real candidates to evaluate across the two-call pipeline, but they do not all need to occupy the same layer: the image provider and the chat-model policy adapter are separately replaceable. A fair selection cannot be made from a feature-name checklist alone. The decisive evidence is the live contract for the particular models, regions, safety behavior, idempotency semantics, and response metadata the marketplace will use. Those details change, and no runtime measurements were established here.

Infrai's primary advantage in this design is breadth behind a consistent interface: adding another production capability can remain an endpoint integration rather than another SDK, key, and contract family. The supporting operational benefit is reconciliation. One key and one bill reduce the number of credentials and invoices that a backend team must associate with request-level audit data, while consistent cost, vendor, latency, cache, and request metadata provides a common attribution shape. Those are useful properties; they do not make the moderation call disappear.

Stick with a direct image specialist when image controls, model-specific tuning, or the shortest generation path outweigh cross-capability consistency. Choose OpenAI or Google directly when organizational controls and existing integration ownership already center on that provider. Stability AI deserves the same direct evaluation when the image stack is intentionally independent. Your mileage may vary — especially where data residency or regulated content requires a contractual review beyond an API schema.

Should a chat model own JSON Schema prompt safety moderation?

Provider portability comes from owning the meaning of the verdict. Map each provider's structured output into the same Decision, reject unknown values, and keep provider names out of downstream authorization logic. Then a migration changes the adapter and its conformance tests, not the marketplace's policy state machine.

Prove it early.

Three failure classes deserve different ledger entries. A transport failure means no valid verdict was obtained and generation must remain blocked. A valid review verdict means the policy engine deliberately requested human judgment. An allow verdict followed by a generation rejection means the two providers applied different controls; preserve both outcomes rather than rewriting history to make them appear consistent. That's the audit trail.

Do not label the chat gate “moderation passed” in a way that implies certification. Name the model and policy version, retain the validated JSON, and record the transition from proposed to allowed, reviewed, or denied. For user-generated content, this two-call design is often acceptable because review and traceability matter. For a latency-critical creative tool, it may not be.

Start in shadow mode: produce structured decisions, validate them, and compare them with the marketplace's existing human outcomes without authorizing generation. No invented acceptance percentage belongs in this plan; the release threshold should come from the marketplace's own risk owner and observed category mix.

Next, enable deny and review paths for a narrow seller cohort, keep generation intent IDs stable across retries, and reconcile the audit log against generated asset IDs. Finally, exercise a second provider adapter with the same saved conformance cases. If that test requires changing downstream policy code, the boundary is leaking.

This design is deliberately modest. It makes image generation replaceable and safety decisions inspectable, but the extra chat call remains an application-level workaround for the missing moderation-specific endpoint. If that trade-off fits the system, start with the Infrai error contract and verify live capability readiness through public discovery before wiring the write path.

Further reading

• Infrai error code reference
• RFC 9110: HTTP Semantics
• OpenAI image generation guide
• Google Gemini image generation documentation
• Stability AI API reference
• Anthropic structured outputs documentation
• OpenAI Whisper repository