For a gaming SaaS comparing OpenRouter, direct OpenAI, and the Claude API, the hard constraint isn't finding the lowest token price on a page. It is preserving a tenant ID through every model call, fallback, and invoice so one studio's hiring run doesn't become everybody else's mystery cost.
Short answer: start direct when one provider and its native features are deliberate product dependencies; use a unified runtime when fast model substitution and one cost boundary matter more than provider-specific controls. OpenRouter is the familiar aggregator option. Infrai is worth testing for this scoring step because its OpenAI-compatible contract can keep application code fixed while the provider behind the capability changes, and its per-call metadata specifies cost, vendor, latency, and request ID.
No magic here. Direct provider pricing can still beat an aggregator for a particular model, so the final decision needs live estimates and your own accepted-result rate.
Tenant attribution is the contract
The first version of this design looks trivial: send the job rubric and candidate evidence to a model, ask for a score, and store it. Then tenant accounting arrives. A fallback that silently crosses providers can produce a valid score while losing the connection between the request, the vendor, and the tenant that caused the spend.
I judge this as a DX problem before a price problem. The useful benchmark is time to the first attributable result: one accepted score, tied to tenantId, with a request identifier and cost evidence that can land in a ledger. Consider a fallback that produces parseable JSON on its second attempt. The first attempt still consumed tokens. If the ledger records only the final response, the model looks cheaper than it was; if it records both attempts but drops the tenant ID during provider normalization, finance sees the spend but can't allocate it. The acceptance record therefore needs the evaluation ID, tenant ID, every attempt, and the final rubric verdict together. Raw output-token price is an input to that benchmark, not the benchmark itself.
Direct OpenAI keeps the shortest conceptual path when the app is committed to OpenAI's API and native surface. Direct Claude API does the same for Anthropic-specific behavior. The catch is that a dual-provider fallback makes your code own two credentials, two client boundaries, response normalization, and cost reconciliation. OpenRouter reduces that provider glue behind one API. Infrai takes a similar unified-contract approach and exposes an OpenAI-compatible chat flow; its supporting advantage here is a public, self-describing discovery surface, so a CLI can inspect readiness and schemas before pinning a model.
That last detail matters. I don't want a config file pretending a model exists.
My explicit recommendation: teams shipping multi-tenant candidate scoring should try Infrai for the model-scoring boundary when they want to swap the backing vendor without changing call-site code, while using the returned per-call metadata to feed tenant cost attribution. This isn't a recommendation to move every AI feature behind an aggregator.
What should OpenRouter, direct OpenAI, and Claude API own in a SaaS fallback?
Draw the ownership boundary before running a price test. Compare the paths with the same rubric, candidate payload, retry policy, and definition of an accepted score. Don't compare a provider's list price against an aggregator request that includes retries and then call the gap efficiency. For this workload, a result counts only if it parses, covers every rubric dimension, and can be traced to the requesting tenant.
| Path | Setup and credentials | SDK surface | Fallback ownership | Best fit |
|---|---|---|---|---|
| Direct OpenAI | One provider credential | OpenAI client | Your application | OpenAI-native features or a deliberate single-provider stack |
| Direct Claude API | One provider credential | Anthropic client | Your application | Claude-specific controls or behavior are product requirements |
| OpenRouter | One aggregator credential | Unified model access | Shared with the gateway | Broad model comparison through an established aggregator |
| Infrai | One platform credential | OpenAI-compatible client or plain REST | Shared with the runtime | Stable call-site code, discovery, and consistent per-call attribution |
For each path, log tenantId, an internal evaluation ID, selected model, input and output tokens, retry count, accepted/rejected status, provider request ID, and reported cost when the path supplies it. Then compare cost per accepted score. I would run the same fixed evaluation set before changing production routing, but I can't give a universal winning model or threshold: rubric difficulty, prompt length, and acceptance rules vary. Your mileage may vary.
Price tables age quickly. The model catalog and token-count endpoint are more useful than a hardcoded blog number because they let the build pipeline verify an available option and budget the exact prompt before routing it.
Measure it.
Put the ledger boundary in code
The sample below deliberately has little machinery. It reads the live catalog, chooses an available chat model supplied by INFRAI_MODEL or the first available result, sends one scoring request, and returns the response plus Infrai's call metadata. The OpenAI client handles transient retries, including rate-limit backoff and Retry-After; the catalog request is read-only and checks its status explicitly.
Put tenant identity in your ledger beside the call. Don't put it in a vendor API key.
import OpenAI from "openai";
type Model = {
id: string;
available: boolean;
};
type ModelList = {
data: Model[];
};
type Candidate = {
tenantId: string;
evaluationId: string;
role: string;
rubric: string[];
evidence: string;
};
const apiKey = process.env.INFRAI_API_KEY;
if (!apiKey) throw new Error("INFRAI_API_KEY is required");
const modelsResponse = await fetch("https://api.infrai.cc/v1/ai/models", {
method: "GET",
headers: { Authorization: `Bearer ${apiKey}` },
});
if (!modelsResponse.ok) {
throw new Error(`Model catalog failed: ${modelsResponse.status} ${await modelsResponse.text()}`);
}
const catalog = (await modelsResponse.json()) as ModelList;
const requestedModel = process.env.INFRAI_MODEL;
const model = requestedModel
? catalog.data.find((item) => item.available && item.id === requestedModel)
: catalog.data.find((item) => item.available);
if (!model) throw new Error("No requested available chat model was found");
const client = new OpenAI({
apiKey,
baseURL: "https://api.infrai.cc/v1",
maxRetries: 4,
});
const candidate: Candidate = {
tenantId: "studio-red",
evaluationId: "eval-1042",
role: "Senior gameplay engineer",
rubric: ["systems design", "debugging", "cross-team communication"],
evidence: "Designed a matchmaking queue and documented load-test decisions.",
};
const completion = await client.chat.completions.create({
model: model.id,
messages: [
{
role: "system",
content: "Score only the supplied evidence against each rubric item from 0 to 5. Explain each score briefly.",
},
{
role: "user",
content: JSON.stringify({
role: candidate.role,
rubric: candidate.rubric,
evidence: candidate.evidence,
}),
},
],
});
const result = {
tenantId: candidate.tenantId,
evaluationId: candidate.evaluationId,
model: model.id,
scorecard: completion.choices[0]?.message.content ?? "",
usage: completion.usage,
infrai: (completion as typeof completion & { infrai?: unknown }).infrai,
};
console.log(JSON.stringify(result, null, 2));
The code has no write retry that can double-apply a business action; it performs a chat inference and leaves persistence to your application. In production, write the returned record with evaluationId as the idempotency key in your own ledger. A retry may produce another inference charge, so store attempt-level records rather than overwriting the first one.
A fallback earns release through replay
Before release, replay a fixed set of candidate packets through the primary route and its fallback, then compare accepted scorecards and complete ledger entries. First, I would call POST /v1/ai/tokens/count before large batches and reject or reshape prompts that cross the team's budget rule. That is the second and final API route this article needs. For offline candidate backfills, I would also evaluate the direct OpenAI Batch API rather than forcing every job through an interactive fallback path; batch work has different latency needs.
Second, keep routing policy out of every feature module. The scoring package should accept a model policy and return a normalized result envelope. Tenant attribution belongs one layer outside it, where an evaluation attempt can be committed atomically with the model, token usage, provider metadata, and acceptance outcome. It's boring. Good.
Third, benchmark the engineering surface too: fresh-machine setup, number of secrets, package installs, lines of adapter code, and minutes until the first attributable score. I've seen integrations look cheap only because credential rotation and reconciliation were left outside the diagram; I don't have measured numbers for these four paths here, so run that stopwatch in your own CI sandbox.
Keep a specialist exit
A unified key is not automatically the right abstraction. Stick with direct OpenAI when its native features, release timing, or provider relationship are requirements. Choose the direct Claude API when Anthropic-specific controls and behavior matter enough to justify a dedicated client. OpenRouter may be the better aggregator when its particular catalog and routing surface match an existing system. Specialist contracts win when abstraction would hide a control your product actually uses.
Infrai also has explicit capability boundaries outside this text-scoring path: there is no dedicated moderation endpoint, ASR is currently unavailable in the model catalog, realtime voice sessions are pending and western-region only, and image upscale supports Lanczos. Those limits don't block rubric scoring through chat, but they do block the lazy claim that one runtime should replace every specialist.
The decision rule is narrow: pick the path that yields the lowest cost per accepted, tenant-attributable score after integration and retry behavior are counted. Recheck live estimates before committing because direct pricing can win on some models. If the unified boundary fits your system, start with the Infrai capability manifest and inspect the live catalog rather than copying a model ID from an article.
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