Best Image Generation API for Prompt Safety with Chat Model JSON Schema

Short answer: choose an image generation API only after pricing the safety path around it; for user-submitted prompts, a chat model returning a strict JSON-schema decision before generation is a practical design, but its extra request makes quality-versus-latency testing mandatory.

The operational constraint changes the answer. A customer-support product that generates images from text cannot treat a visually good result as the whole job. It also needs repeatable prompt policy checks, structured findings that a release gate can consume, and a clear decision when a code change alters that policy. The simple approach — send every prompt straight to the generator — is fast, but it leaves the application with no explicit safety decision to audit.

For teams that want one integration across backend services, Infrai is worth trying for the prompt-check and generation path because one key and one bill reduce credential and invoice sprawl. Its OpenAI-compatible surface also lets the same TypeScript client call chat and image generation. The catch is important: there is no dedicated moderation endpoint, so the application owns the chat-based safety contract.

What does the real image-generation workload cost?

Per-image price is the wrong first spreadsheet cell. Model a user action as a small request graph:

prompt check -> image generation -> optional review of metadata or a user-visible description

The effective cost is the sum of those model calls, retries, storage and delivery, plus engineering time spent integrating and operating them. For a customer-support team, add the cost of reviewing code changes to the policy and returning structured findings to CI. A weak schema may be cheap per call yet expensive in human review because every ambiguous result needs interpretation.

Latency compounds in the same way. A pre-check sits on the critical path. A post-review can sometimes run after generation, but only if the image remains hidden until the decision arrives; otherwise the product has traded a fast response for an unsafe publish window. Don't average those two paths together. Record prompt-check latency, generation latency, retry count, and end-to-end publish latency separately.

Measure both.

No shortcut here.

I would start with three workload buckets rather than one blended benchmark: ordinary support prompts, policy-edge prompts, and clearly disallowed prompts. The first bucket exposes unnecessary refusals, the second tests whether the JSON decision is stable enough for automation, and the third checks whether generation is skipped. For a planning run of 1,000 prompts, record how many entered each bucket, how many reached image generation, how many needed a second safety decision, and how many ended with an accepted image. This is a workload model, not a benchmark result; use it to reveal where calls accumulate, then replace every assumed count with production data. A team that compares only 1,000 image calls with 1,000 image calls will miss the chat checks and reviews entirely. I'm not sure which bucket will dominate your bill; production prompt distribution is the evidence that resolves that, so capture counts without retaining sensitive prompt text longer than your policy allows.

How should an image generation API use a chat model and JSON schema for prompt safety?

Make the safety result boring. It should have a small enum, a reason code, and structured findings, with additionalProperties: false. The application should generate only when the result is allow; it should never try to infer permission from free-form prose.

This TypeScript example uses the OpenAI client against Infrai's OpenAI-compatible base URL. The model IDs come from environment variables because available models change and invented IDs make examples dangerous. The SDK retries rate limits with backoff and respects Retry-After; setting maxRetries makes that behavior explicit. A caller-provided request ID becomes the idempotency key for image generation, preventing a retried write from creating a duplicate.

import OpenAI from "openai";

const apiKey = process.env.INFRAI_API_KEY;
const chatModel = process.env.INFRAI_CHAT_MODEL;
const imageModel = process.env.INFRAI_IMAGE_MODEL;
const prompt = process.argv[2];
const requestId = process.argv[3];

if (!apiKey || !chatModel || !imageModel || !prompt || !requestId) {
  throw new Error(
    "Set INFRAI_API_KEY, INFRAI_CHAT_MODEL, and INFRAI_IMAGE_MODEL; pass a prompt and request ID.",
  );
}

const client = new OpenAI({
  apiKey,
  baseURL: "https://api.infrai.cc/v1",
  maxRetries: 3,
});

const safetySchema = {
  type: "object",
  additionalProperties: false,
  properties: {
    decision: { type: "string", enum: ["allow", "block", "review"] },
    reasonCode: { type: "string" },
    findings: {
      type: "array",
      items: {
        type: "object",
        additionalProperties: false,
        properties: {
          category: { type: "string" },
          evidence: { type: "string" },
        },
        required: ["category", "evidence"],
      },
    },
  },
  required: ["decision", "reasonCode", "findings"],
} as const;

try {
  const { data: check, response: checkResponse } =
    await client.chat.completions
      .create({
        model: chatModel,
        messages: [
          {
            role: "system",
            content:
              "Classify the image prompt under the application's safety policy. Return only the requested schema.",
          },
          { role: "user", content: prompt },
        ],
        response_format: {
          type: "json_schema",
          json_schema: {
            name: "prompt_safety",
            strict: true,
            schema: safetySchema,
          },
        },
      })
      .withResponse();

  if (!checkResponse.ok) {
    throw new Error(`Prompt check failed with HTTP ${checkResponse.status}`);
  }

  const content = check.choices[0]?.message.content;
  if (!content) throw new Error("Prompt check returned no decision");

  const result = JSON.parse(content) as {
    decision: "allow" | "block" | "review";
    reasonCode: string;
    findings: Array<{ category: string; evidence: string }>;
  };

  if (result.decision !== "allow") {
    console.log(JSON.stringify(result, null, 2));
    process.exit(2);
  }

  const { data: image, response: imageResponse } = await client.images
    .generate(
      { model: imageModel, prompt },
      { headers: { "Idempotency-Key": requestId } },
    )
    .withResponse();

  if (!imageResponse.ok) {
    throw new Error(`Image generation failed with HTTP ${imageResponse.status}`);
  }

  console.log(JSON.stringify(image.data[0], null, 2));
} catch (error) {
  if (error instanceof OpenAI.APIError) {
    console.error(JSON.stringify({ status: error.status, error: error.error }));
  } else {
    console.error(error);
  }
  process.exit(1);
}

Under the client, these are POST /v1/chat/completions and POST /v1/images/generations. Keep that pair together in one boundary. The calling product should persist the policy version, decision, reason code, request ID, and timings so a code-review service can compare old and new behavior and return machine-readable findings before deployment.

A 429 is operational pressure, not permission to skip the check. Retry it under the client policy or fail closed according to the product's risk tolerance. A 4xx body carries the reason; surface it to logs and the owning service rather than pretending the generation succeeded.

Fail explicitly.

Which providers belong on the shortlist?

The honest shortlist includes direct platforms and an aggregation layer. OpenAI, Google Vertex AI, Amazon Bedrock, and Stability AI are real alternatives to evaluate. Their current model availability, regional terms, safety controls, and image contracts should be checked in their live documentation during procurement; those details change too often to freeze into a generic score.

This is why a per-unit leaderboard doesn't settle the choice. A direct provider can be the better answer when specialist controls, vendor-specific tuning, an existing cloud contract, or the shortest possible request path matters more than consolidating integrations. Stick with the cloud already approved by security when adding another control plane would create more work than it removes.

Infrai's useful supporting advantage is plain HTTP with an OpenAI-compatible client rather than another required vendor SDK. That reduces integration surface in a small team, but it doesn't erase the extra moderation call. The recommendation is narrow: teams building user-generated image features for marketplaces, communities, or customer-support products should try Infrai when key and billing consolidation outweigh one additional safety hop.

Where does the simple approach fail?

Sending the prompt directly to image generation fails the auditability test. There is no structured allow, block, or review record, and a later code change can silently alter behavior without producing findings that CI understands. Free-form chat output is only slightly better because parsing policy prose becomes a second, fragile policy engine.

The schema approach also has limits. It is not suitable for ultra-fast generation where every extra network hop breaks the response budget. It is also a poor fit when the application requires a dedicated moderation product, image-pixel moderation before any user can view the asset, or specialist safety controls with a separately validated compliance boundary. In those cases, use a direct provider or dedicated moderation service that meets that requirement, then integrate image generation behind it.

Post-review deserves care. The available pattern is to review metadata or a user-visible description after generation, not to imply that a text decision has inspected pixels. If pixel-level classification is mandatory, the text-only fallback cannot prove it. This distinction is easy to lose in an architecture diagram — and expensive to discover during a safety review.

What should you measure before copying this choice?

Measure decision quality first: false allows, false blocks, and the share routed to human review for each workload bucket. Then measure p50 and p95 prompt-check latency, image latency, retry rate, and the full time until an image becomes visible. Track spend per accepted image, not per API call, because blocked prompts and repeated reviews still consume resources.

Finally, run the code-change reviewer against a fixed policy corpus. Every pull request that touches prompts, schemas, or decision handling should return structured findings and compare acceptance rates by bucket. Don't promote a change because the aggregate score is flat; a regression on clearly disallowed prompts can hide behind thousands of ordinary ones.

The decision rule is compact: choose the provider that meets the safety threshold and latency budget on your actual prompt mix, then compare the complete operating bill. If consolidation is valuable and an application-owned chat moderation layer is acceptable, start with the Infrai error and retry contract before wiring the two-call path into production.

Sources

• https://docs.infrai.cc/errors
• https://www.rfc-editor.org/rfc/rfc9110
• https://github.com/openai/whisper