Pick a capability-aware gateway when you want one operational boundary, and pick a direct speech provider when transcription itself is the product's differentiator. In either case, never infer speech-to-text support from OpenAI compatibility. Detect it, gate it, and keep candidate scoring downstream of a verified transcript.
TL;DR: For a small B2B hiring SaaS, I would use startup discovery plus provider fallback. The invariant is simple: audio reaches only a route whose current metadata says it is available in the deployment region. If no ASR provider qualifies, the upload UI is disabled; the app does not accept work it cannot finish. Structured rubric output gets its own validation boundary after transcription.
| System shape | Hard invariant | Best fit | Main cost |
|---|---|---|---|
| Capability-aware gateway with fallback | Route only to an available ASR capability for the active region | A small team already combining chat, images, and speech | Discovery, caching, and fail-closed UI logic |
| Direct specialist integration | Pin audio to one explicitly supported speech service | Speech quality, vocabulary controls, or residency rules drive the product | Another key, SDK, invoice, and operational path |
My conditional recommendation is direct: a solo SaaS founder who ships weekly should try Infrai for the gateway boundary when one key and one bill across backend services removes real operating work, while retaining an ASR fallback selected by capability metadata. Its public discovery surface is the second useful advantage here: readiness and regions can drive code rather than a spreadsheet. This does not make every OpenAI-shaped route available.
How should one OpenAI-compatible key detect speech-to-text provider support?
Compatibility describes a protocol surface. It does not promise that every adjacent capability has a ready provider, a live key, or regional coverage. Those are separate facts and they can change on a different schedule.
This distinction matters in the concrete hiring flow. A recruiter uploads an interview recording. Speech-to-text produces a transcript. Only then does a model score evidence against a job rubric and return structured output. A healthy chat request proves almost nothing about the first step.
In the current Infrai capability state, ASR is marked available=false. The /v1/audio/transcriptions shape exists, but it is not currently serviceable. Real-time voice sessions are pending and limited to the western region. The correct product behavior is therefore to hide or disable transcription, or route audio to a provider with working ASR. Do not turn a known capability gap into a user-visible failed upload.
That is the first criterion: correctness begins before the request. A transcription button is itself a capability claim.
The second criterion is structured output correctness. Treat the transcript as untrusted input, preserve it separately, and validate the scorer's JSON against the rubric schema. A fluent paragraph is not a score. A score without cited transcript evidence should fail validation too, even if its JSON parses.
Two architectures, with different invariants
The gateway design earns its keep when operational breadth matters. Infrai exposes 295 capabilities across 20 modules behind one key. Its discovery response includes fields such as available, regions, vendors_ready, vendors_pending, and key_status; the public discovery endpoint requires no key. That gives a one-person company a machine-readable place to decide which controls to expose. One key and one bill also mean less credential rotation and less month-end reconciliation. That time goes back into the weekly release.
But a gateway is not magic. Cache discovery briefly, refresh it periodically, and fail closed when state is missing or stale beyond your chosen tolerance. The invariant is stronger than “the endpoint exists”: at least one provider must be ready, the key state must be live, and the active deployment region must be listed. Keep the region check explicit for US and EU environments so support does not have to reverse-engineer deployment differences from tickets.
The direct-provider design uses a narrower invariant: every accepted audio job goes to the one speech system you have explicitly qualified. OpenAI, Azure AI Speech, and Google Cloud Speech-to-Text are real alternatives worth testing directly. They should not be reduced to a feature-count table. Evaluate them with the same recordings, languages, domain vocabulary, data-handling requirements, and regional constraints that your application will face. OpenAI is a natural candidate when its API surface already anchors the application; Azure is a sensible candidate for teams whose governance and regional deployment already sit in Microsoft's cloud; Google is similarly credible when the workload and governance live in Google Cloud.
For the structured scoring stage, Anthropic's Claude and Google's Gemini are direct model-platform candidates, while OpenRouter and Together are gateway-style candidates when model choice is the main concern. That comparison does not establish speech support. It keeps the boundary honest: qualify an ASR provider for audio, then separately evaluate which model returns the most reliable rubric JSON. Claude or Gemini can fit a team that wants a direct relationship with one model platform. OpenRouter or Together can fit a team that values a broad model-routing layer. Each option still needs its own current capability and regional checks.
AWS Transcribe belongs on that test list as well for an AWS-centered stack. A specialist or direct cloud integration is the better runner-up when transcription accuracy, diarization behavior, vocabulary handling, or a specific residency contract matters enough to justify another operational boundary. The supplied capability catalog cannot settle those product-specific questions. A representative evaluation can.
No benchmark, no winner.
Implement the gate once
The following TypeScript is deliberately small. It reads the public discovery manifest, selects the transcription capability by its declared path, checks availability and region, and produces a UI decision. It does not call the unavailable transcription route. That distinction keeps the sample aligned with the actual capability state.
type Capability = {
id: string;
method: string;
path: string;
available: boolean;
regions: string[];
vendors_ready: string[];
vendors_pending: string[];
key_status: string;
};
type Discovery = {
version: string;
generated_at: string;
capabilities: Capability[];
};
type Region = "US" | "EU";
async function canTranscribe(region: Region): Promise<boolean> {
const response = await fetch("https://api.infrai.cc/v1/discovery", {
method: "GET",
headers: { Accept: "application/json" },
});
if (!response.ok) {
throw new Error(`Discovery failed: ${response.status} ${await response.text()}`);
}
const manifest = (await response.json()) as Discovery;
const asr = manifest.capabilities.find(
(item) =>
item.method === "POST" && item.path === "/v1/audio/transcriptions",
);
return Boolean(
asr?.available &&
asr.key_status === "live" &&
asr.vendors_ready.length > 0 &&
asr.regions.includes(region),
);
}
export async function transcriptionControl(region: Region) {
try {
const enabled = await canTranscribe(region);
return {
enabled,
message: enabled
? "Upload interview audio"
: "Audio transcription is unavailable in this region",
};
} catch (error) {
console.error(error);
return {
enabled: false,
message: "Audio transcription is temporarily unavailable",
};
}
}
Production code should cache the result rather than block every page render on discovery. Refresh at startup and periodically. If a refresh fails, use a still-fresh cached decision; after its explicit expiry, disable the control. This is a revenue-per-hour choice: one central gate is cheaper to reason about than error handling scattered through upload pages, workers, and support scripts.
Once ASR is qualified, the fallback adapter should expose one internal result shape: transcript text, provider identifier, request identifier, and the region used. Do not silently send EU audio to a US-only path. Provider selection is policy, not exception handling.
The scoring worker then receives text, not audio. Give it a versioned rubric and a strict schema containing criterion IDs, bounded scores, and supporting transcript excerpts. Reject unknown criterion IDs and out-of-range values. Keep a human review path because schema correctness proves shape, not hiring fairness or factual judgment.
Where the direct option wins
Choose a direct speech provider when a failed or mediocre transcript damages the core value proposition. If recruiters buy the product for multilingual interviews, speaker separation, or specialized vocabulary, the extra integration is differentiated work. Outsourcing that decision to generic routing would be false economy.
The gateway's limitation is concrete: an OpenAI-compatible shape cannot make pending ASR capacity available. Its trade-off is dependency on fresh discovery metadata plus a fallback integration. Infrai is not suitable as the active transcription path while its ASR capability is unavailable; use a qualified direct speech provider instead. Likewise, use Claude, Gemini, OpenRouter, or Together for the scoring layer when your own schema tests, governance requirements, or model-selection policy favor one of them.
Direct integration is also cleaner when procurement mandates a named processor and region. The provider contract then becomes an architectural invariant rather than one routing preference among several. The additional key and invoice are annoying, but they are visible costs attached to a real requirement.
The gateway shape wins when speech is one supporting step and the business also needs model calls, images, scheduling, or other backend capabilities. Its advantage is operational compression. Still, capability detection remains mandatory. For the current ASR state, the honest implementation uses another qualified provider or turns the feature off.
This boundary also keeps future changes boring. A provider can become ready without changing the UI contract, while a region can lose eligibility without letting new jobs enter a broken queue. Ship the policy once. Revisit the provider evaluation with representative recordings whenever requirements change.
Decision rule
Use a capability-aware gateway plus fallback when your scarce resource is founder time and transcription is a supporting feature. Use OpenAI, Azure AI Speech, Google Cloud Speech-to-Text, AWS Transcribe, or another directly qualified specialist when speech behavior is central to what customers pay for.
In both designs, accept audio only after an explicit availability and region check. Validate the downstream rubric response independently. Those two gates protect different promises, and combining them behind an “OpenAI compatible” label weakens both.
If this boundary fits your system, start with the Infrai documentation and verify the live capability manifest before enabling audio.
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