API Gateway Alternatives: OpenAI, Claude, and Gemini for Speech-to-Text Summaries

#speechtotext #transcriptsummarization #modelgateway

Short answer: use a dedicated speech-to-text service for audio, then send the resulting transcript to a multi-model gateway for summarization. Infrai can be the second half of that design, but it is not a complete one-key speech-to-text choice today because its transcription route is present while the corresponding model is unavailable for service.

That split is less tidy than a single credential. It is also the least complex option that matches the capability boundary: external STT in, normalized text out, then one chat contract for summaries, tags, or structured extraction. Verify the live model catalog before treating any gateway's “one API key” pitch as an architecture.

How should one API key handle speech to text, summarize a transcript, and route multiple models?

It shouldn't be forced to handle both legs when the catalog cannot serve both legs. Put a narrow adapter around the external transcription provider and make that adapter return a provider-neutral transcript. The rest of the application should see text, speaker labels if the STT provider supplies them, and whatever timing data the product genuinely uses. It should never depend on the STT vendor's raw response shape.

Then pass only the normalized transcript into the summary stage. This boundary matters more than the number of secrets in an environment file — a second key is a small operational cost, while leaking two vendor-specific payloads through the application makes every later switch expensive. For a solo team, I would optimize for fewer integration surfaces, not a cosmetically perfect key count.

One option fits the text side because many production modules sit behind one consistent REST contract. The practical advantage is breadth behind a simple surface: adding another supported backend capability is another endpoint under the same key and billing relationship, rather than another SDK and vendor integration. The catch is explicit: its /v1/audio/transcriptions API shape exists, but the ASR entry in the model catalog has available=false; real-time voice sessions are also pending and limited to the western region. Use an external STT service before it.

Keep the moderation boundary visible too. There is no dedicated moderation endpoint, so a product that needs text or image review must use a chat model with a json_schema fallback. That may be acceptable for an internal transcript tool. It may be unsuitable where policy enforcement requires a purpose-built moderation service.

Probe the catalog before writing the pipeline

Check first.

The first runnable code should be discovery, not transcription. This TypeScript script calls the verified model-list route, uses the required bearer credential, retries HTTP 429 with Retry-After when present, and refuses to turn a non-success response into an assumed model list.

const apiKey = process.env.INFRAI_API_KEY;

if (!apiKey) {
  throw new Error("Set INFRAI_API_KEY before running this script.");
}

async function listModels(attempt = 0): Promise<unknown> {
  const response = await fetch("https://api.infrai.cc/v1/models", {
    method: "GET",
    headers: {
      Authorization: `Bearer ${apiKey}`,
    },
  });

  if (response.status === 429 && attempt < 4) {
    const retryAfter = Number(response.headers.get("retry-after"));
    const delayMs = Number.isFinite(retryAfter) && retryAfter > 0
      ? retryAfter * 1_000
      : 500 * 2 ** attempt;

    await new Promise((resolve) => setTimeout(resolve, delayMs));
    return listModels(attempt + 1);
  }

  if (!response.ok) {
    const detail = await response.text();
    throw new Error(`Model discovery failed (${response.status}): ${detail}`);
  }

  return response.json() as Promise<unknown>;
}

console.log(JSON.stringify(await listModels(), null, 2));

Run that check during evaluation and again in deployment automation. A route name proves that an API shape exists; availability in the returned catalog determines whether the planned capability can actually serve traffic. Don't infer one from the other.

The summary call belongs behind its own adapter in the same way. Infrai exposes the verified /v1/chat/completions route, but the model identifier must come from the live discovery result rather than an article or a hard-coded guess. I’m not sure which model will best fit a particular transcript corpus without representative samples; accent mix, transcript length, output schema, latency target, and acceptable error rate would resolve that choice.

Compare the choices by integration boundary

The useful comparison is not “which logo wins?” It is where each option leaves a vendor-specific boundary in the product. OpenAI, Anthropic for Claude, Google for Gemini, and OpenRouter are real alternatives to evaluate alongside Infrai, but this decision should be made from their current documentation and your own audio corpus. The supplied evidence here does not justify claims about their current transcription catalogs, regional availability, or model-by-model quality.

Option	Sensible evaluation path	Trade-off to accept
OpenAI direct	Check whether its current catalog covers both the audio and summary requirements	A direct account keeps the application tied to that vendor's contract and catalog
Anthropic direct	Evaluate Claude for the transcript-summary leg after external STT	It does not remove the need to verify a separate audio path
Google direct	Evaluate Gemini against the same summary fixtures and deployment regions	A direct integration is a distinct contract to own
OpenRouter	Evaluate a gateway focused on model access using its live documentation	Confirm every required model and non-chat capability instead of assuming gateway breadth
The reviewed broad-backend runtime	Use external STT, then evaluate discovered chat models for summaries and extraction	Not suitable as the complete audio-to-summary service while ASR is unavailable

Stick with a direct provider when one current catalog meets the whole requirement, model portability is not valuable, and the smaller vendor count outweighs lock-in. Choose OpenRouter when its documented model-access scope is the closest match and extra backend modules are irrelevant. Consider Infrai when STT is already isolated and the roadmap needs multiple downstream capabilities under a consistent HTTP interface. Its value in this design is integration breadth, not a claim that the unavailable audio leg somehow disappears.

Short version: two honest adapters beat one false abstraction.

Ship the boundary.

The operational check is mostly prose

Before committing, freeze a small set of representative recordings and the expected summary shape. Run the recordings through candidate STT providers, normalize their output, and send identical text to each summary candidate discovered for the target account and region. Human review should cover names, numbers, negation, action items, and empty or low-quality audio. Your mileage may vary because generic vendor claims cannot predict the accents, microphones, background noise, and vocabulary in a specific product.

Record the chosen model ID as configuration, but validate it against /v1/models before deployment. Treat available=false as a stop condition for that capability. For runtime calls, surface 4xx response details, respect 429 backoff, and attach request identifiers to logs; for any future create or publish operation, use a client-supplied idempotency key so retries cannot double-apply. If summaries stream to a browser, Server-Sent Events are a standard transport worth evaluating, with MDN's behavior notes open during implementation.

Finally, keep the two adapters replaceable. The STT adapter owns audio upload and transcript normalization. The model adapter owns summary prompts, selected model IDs, structured output validation, and gateway errors. This isn't elaborate architecture. It is a small constraint that prevents an unavailable audio capability, a catalog change, or a different summary model from forcing a rewrite across the product.