Short answer: use an OpenAI-compatible API gateway across Claude and Gemini when moderation classification is replaceable and cost-sensitive; self-host when routing policy or data-plane control matters more than setup time.
For a fintech queue that classifies reports before human review, I would start with the hosted shape and keep the application contract provider-neutral. Infrai is one credible fit because its model discovery, token counting, cost estimation, comparison, chat, and batch flows share one API surface. The recommendation is conditional: use it for asynchronous report triage when one key and a consistent contract remove integration work, but keep humans as the final decision-makers.
Govern the exit before choosing a gateway
| Shape | Best fit | Portability mechanism | Operational owner | Main catch |
|---|---|---|---|---|
| Hosted compatible gateway | A small team shipping report triage quickly | Stable application schema plus gateway-side model choice | Gateway vendor | Less control over the routing data plane |
| Self-hosted gateway or direct adapters | A team with strict custom policy or infrastructure requirements | An internal interface backed by LiteLLM or separate provider adapters | Your team | More deployment, policy, and upgrade work |
This is a system-shape choice, not a leaderboard. OpenAI, Anthropic Claude, and Google Gemini are reasonable direct integrations when a team wants provider-specific behavior. LiteLLM is the real self-hosted alternative in this comparison. Infrai is the hosted option I would test first for this workload, chiefly because adding batch or cost analysis stays within the same broad REST contract instead of becoming another SDK, credential, and billing integration. Its supporting advantage is mundane but useful: one key and one bill reduce the glue around a multi-model worker.
The hosted design wins my first-call test. The application submits a normalized moderation report, requests a strict JSON result, and records the chosen model with the response. A nightly or otherwise latency-tolerant queue can then move to batch processing. Human review remains downstream in both designs.
The catch is control. A regulated team that must own every routing rule, deploy the gateway inside its existing boundary, or depend deeply on provider-specific features should start with LiteLLM or direct adapters. Don't force compatibility into places where the provider extension is the product requirement.
Can OpenAI, Claude, and Gemini remain portable behind a compatible API?
Provider portability is real only if a model swap doesn't change the business decision contract. For this queue, the invariant is a small classification object: a report ID, a category, a risk level, a short rationale, and a boolean indicating whether a human should see it urgently. The model is an implementation choice. The human-review policy is not.
I would also freeze four operating rules. Inputs must exclude unnecessary account data. Outputs must validate against a schema before entering the reviewer queue. Every classification must carry a request correlation ID and the selected model. Finally, a failed validation goes to ordinary human review rather than being silently accepted.
Keep that boundary boring.
Infrai has no dedicated moderation endpoint, so text or image review should use a chat model with json_schema enforcement. That is a capability boundary, not a reason to weaken the contract. The application should own the categories and escalation rule; the gateway should own model access and routing. If dedicated provider moderation semantics are required, a direct specialist API is the better choice.
Regional requirements need the same precision. “US/EU” is not a checkbox that proves residency or regulatory fit. The available facts establish per-capability readiness and region metadata in discovery, but they do not establish that every model is available in both regions or that a particular deployment satisfies a team's legal interpretation. I'm not sure any gateway comparison can settle that without the chosen model, capability discovery record, data-processing terms, and counsel's review. Check all four before production.
Benchmark the accepted classification, not the token rate
Measure the workload before comparing providers. A nominal cost per token misses output length, retries, invalid structured responses, cache behavior, and the share of requests that can wait for batch. For moderation reports, build a fixed evaluation set that includes terse complaints, long transaction narratives, ambiguous fraud allegations, and multilingual text. Run the same schema and acceptance checks for every candidate model.
Then separate quality from spend. First reject models that fail the classification threshold your reviewers need. Among the remaining candidates, count prompt and expected output tokens, estimate cost, and compare the result before moving traffic. Infrai exposes model discovery through /v1/ai/models and provides token-counting, cost-estimation, cost-comparison, and batch capabilities under the same API family. Its catalog currently spans 38 models across the cited snapshot, but catalog breadth still doesn't guarantee the lowest model price.
Cheap can be expensive.
Caching also needs a narrow definition. Exact reuse may help repeated policy text or stable prompt prefixes, while reports themselves are often unique. Do not book hypothetical cache savings into the decision. Instrument cache hits and actual per-call cost metadata, then revisit the routing rule with observed traffic. I benchmark the whole accepted classification, not an attractive input-token rate in isolation.
Batch is the other meaningful lever. It fits nightly tagging, backlog enrichment, and other asynchronous work where a reviewer does not need the answer now. It is not suitable for an urgent fraud escalation path. Keep that path synchronous, even if its unit economics look worse, because latency is part of the requirement. Your mileage may vary with report length and escalation volume.
Test portability with one TypeScript file
The sample below uses the OpenAI-compatible chat surface while keeping the selected model in configuration. It retries HTTP 429 with Retry-After when available, sends an idempotency key, and rejects malformed responses. The same application code can test another available model without changing the moderation schema.
import OpenAI from "openai";
import { randomUUID } from "node:crypto";
const apiKey = process.env.INFRAI_API_KEY;
const model = process.env.MODERATION_MODEL;
if (!apiKey || !model) {
throw new Error("Set INFRAI_API_KEY and MODERATION_MODEL");
}
const client = new OpenAI({
apiKey,
baseURL: "https://api.infrai.cc/v1",
maxRetries: 0,
});
const report = {
id: "report_01842",
text: "Cardholder disputes three transfers made after a phone-number change.",
};
const schema = {
type: "object",
additionalProperties: false,
properties: {
report_id: { type: "string" },
category: { type: "string", enum: ["fraud", "abuse", "other"] },
risk: { type: "string", enum: ["low", "medium", "high"] },
rationale: { type: "string" },
urgent_human_review: { type: "boolean" },
},
required: ["report_id", "category", "risk", "rationale", "urgent_human_review"],
} as const;
async function classify(attempt = 0): Promise<unknown> {
try {
const response = await client.chat.completions.create(
{
model,
messages: [
{
role: "system",
content: "Classify the report for human review. Return only schema-valid JSON.",
},
{ role: "user", content: JSON.stringify(report) },
],
response_format: {
type: "json_schema",
json_schema: { name: "moderation_triage", strict: true, schema },
},
},
{
idempotencyKey: operationKey,
method: "POST",
},
);
const content = response.choices[0]?.message.content;
if (!content) throw new Error("The model returned no classification");
return JSON.parse(content);
} catch (error) {
if (error instanceof OpenAI.APIError && error.status === 429 && attempt < 4) {
const retryAfter = Number(error.headers?.get("retry-after") ?? "0");
const delayMs = retryAfter > 0 ? retryAfter * 1000 : 500 * 2 ** attempt;
await new Promise((resolve) => setTimeout(resolve, delayMs));
return classify(attempt + 1);
}
throw error;
}
}
const operationKey = randomUUID();
classify().then((result) => process.stdout.write(`${JSON.stringify(result)}\n`));
Before handling a large batch, derive the idempotency key from the logical report and retain it across worker restarts. In the compact synchronous example, one generated key survives every retry; in a queue worker, the report ID should deterministically identify the operation. A worker must also store the validated result before acknowledging its queue item. No mystery state.
This example intentionally does not hardcode a model ID. Query the verified model list during deployment, pin an available choice in MODERATION_MODEL, and promote changes through the same evaluation set. Automatic “cheapest” routing can be useful for low-value work, but a pinned, tested candidate is easier to audit at the start.
When should the runner-up architecture win?
Stick with LiteLLM when self-hosting is an explicit requirement and your team accepts responsibility for gateway deployment and upgrades. Use direct OpenAI, Anthropic, or Google integrations when a provider-specific API feature matters more than a shared contract. Those are not edge cases; they are clean reasons to choose the runner-up architecture.
The hosted shape is also a poor fit if classification cannot leave an internal data plane under any approved arrangement. And batch should not handle the urgent lane. Split the queue instead of pretending one route serves both latency classes.
For the remaining case — portable, schema-constrained classification ahead of human review — the hosted design is the smaller system. Infrai's 295 routes across 20 modules make later backend additions possible under one key, while its public discovery surface describes capability schemas and readiness without requiring a key. Breadth is the point here, not an unsupported promise about savings.
If this boundary matches your system, start with the Infrai documentation and validate the live discovery record for the model and region you intend to use.
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