Use a durable job queue to accept transcription requests, then apply one coordinated rate limit across every worker that shares the provider quota. Classify HTTP 429 responses before retrying: honor a valid Retry-After for temporary throttling, but stop and address account or billing errors. The example below uses BullMQ and Redis; OpenAI-specific limits and file rules are labeled as such.
Choose the right transcription workflow
A queue is suited to completed recordings that can wait for processing. The request can store a durable audio reference and metadata, while a worker uploads or streams the stored file to a transcription endpoint. For OpenAI’s file transcription flow, the documented endpoint is /v1/audio/transcriptions; its guide states a 25 MB maximum and lists the supported formats. Those constraints apply to that OpenAI flow, not to speech-to-text APIs generally. Check the current provider requirements before accepting a file.
For audio still arriving from a microphone, call, or media stream, OpenAI directs developers to Realtime transcription. A file queue waits for an input to be complete; a real-time flow is designed around an ongoing stream and its lifecycle. Don’t buffer a live stream into a file queue unless the added delay and completeness requirements are acceptable.
Validate file type and size before enqueueing. Keep credentials on the server and out of job data, and store a durable reference to audio rather than a large raw payload in ordinary queue metadata.
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Design the queue around durable, idempotent work
Persist the audio in durable storage and enqueue a small job containing its reference, customer or tenant identifier, and any transcription options. Use a durable queue backend such as Redis with BullMQ so accepted work can outlive an API process restart. A successful enqueue response should mean the queue has accepted the job, not that transcription has already completed.
Protect against duplicate submissions. A client may time out after the server enqueues a job but before it receives the response, then retry. Accept a stable client request identifier or idempotency key and associate it with the created job; a repeated submission with the same key should return the existing job rather than enqueueing a second paid transcription. Define a retention period for idempotency records that matches how long clients may retry. Keep job state and the transcription result available through a status or result endpoint.
Limit aggregate traffic, not each process independently
Worker count and per-process pacing are not substitutes for a shared quota-aware limit. If several processes each send requests at a locally configured rate, their combined traffic can exceed the provider’s allowance. BullMQ documents that its worker limiter is global across workers for the same queue: its example explains that ten workers with a limit of ten jobs per second still process only ten jobs per second for that queue. See BullMQ rate limiting and BullMQ global rate limit.
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OpenAI rate limits vary by model and organization or project. Read the limits and rate-limit response headers applicable to the account and model you actually use rather than copying a universal requests-per-minute value from an example. The provider documentation does not establish one quota for every account or geography. See OpenAI rate limits.
Static and response-driven pacing
| Approach | When it fits | Trade-off |
|---|---|---|
| Configured queue limiter | Use when you know the applicable provider quota and can set a conservative queue-wide rate. | Simple and predictable, but it does not automatically adapt to a changed limit or temporary 429. |
| Manual pause after throttling | Use when provider feedback, such as a valid Retry-After, requires a temporary delay. |
Responds to live feedback, but a queue-wide pause may also slow work unaffected by a model- or account-specific limit. |
BullMQ documents configured and manual rate limiting. Its current guide says QueueScheduler is not required from BullMQ 2.0 onward and group-key limiting was removed from BullMQ 3.0 onward; avoid examples that assume those older behaviors. Check the installed BullMQ version and its matching documentation before relying on a particular option.
Implement a BullMQ worker with bounded retries
The following is a structural example, not a provider-ready transcription client. Replace transcribeStoredAudio with the selected provider’s documented call and error shape. Supply limiter values only after checking the quota for the account and model. The job’s audioRef should resolve to protected, durable storage.
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import { Queue, Worker, UnrecoverableError } from 'bullmq';
import IORedis from 'ioredis';
const connection = new IORedis(process.env.REDIS_URL);
const queue = new Queue('transcriptions', { connection });
// Configure these from the applicable provider quota; do not copy generic values.
const worker = new Worker(
'transcriptions',
async (job) => {
try {
const result = await transcribeStoredAudio({
audioRef: job.data.audioRef,
options: job.data.options,
});
await saveTranscription(job.id, result);
return { completed: true };
} catch (error) {
if (isPermanentAccountOrBillingError(error)) {
throw new UnrecoverableError('Provider account action required');
}
if (isTemporaryRateLimit(error)) {
const delayMs = retryAfterMilliseconds(error) ?? boundedJitteredDelay(job.attemptsMade);
await worker.rateLimit(delayMs);
throw Worker.RateLimitError();
}
throw error;
}
},
{
connection,
limiter: { max: Number(process.env.STT_MAX_JOBS), duration: Number(process.env.STT_WINDOW_MS) },
},
);
// Submit an idempotently identified job after validating and persisting the audio.
await queue.add('transcribe', { audioRef, options }, {
jobId: stableRequestId,
attempts: 4,
backoff: { type: 'exponential', delay: 1_000 },
});The names and signatures shown for manual rate limiting follow BullMQ’s documented pattern: defer work with worker.rateLimit(delay) and throw its special rate-limit error to return the job to waiting. Verify exact imports and API signatures against the BullMQ version installed in your application. Implement retryAfterMilliseconds to parse the provider’s valid delay format, and make the fallback delay exponential, bounded, and jittered. The pseudocode helper functions are intentionally application-specific.
Persist the transcription and any provider request identifier available from the response. Make result writes idempotent too: a worker can lose its connection after the provider returns but before the queue records completion, leading to another attempt.
Classify 429s and other failures before retrying
A 429 is not automatically a signal to keep retrying. Inspect the HTTP status, response body, and provider headers. A temporary rate throttle can be deferred; if Retry-After is present and valid, do not retry sooner than it allows. If it is missing or invalid, use bounded exponential backoff with jitter to spread retries rather than synchronizing workers into a new burst.
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OpenAI distinguishes rate-limit errors from quota or billing problems, which require account action rather than repeated retries. Consult OpenAI’s 429 troubleshooting guidance alongside its rate-limit documentation. Do not treat every 429 identically, and do not leave a permanently failing job cycling indefinitely.
Set one retry budget across the SDK and queue
Retries multiply if both the API client and BullMQ retry the same operation. The official OpenAI Node.js SDK repository documents two default retries for eligible failures, including 429; actual behavior can depend on the installed package version. See the OpenAI Node.js SDK documentation and verify the version you deploy.
Choose which layer owns retries or calculate the combined maximum attempts and delays. For example, if a queue allows four job attempts and the SDK retries each call twice, one job attempt can issue up to three provider calls, for as many as twelve calls across the queue attempts, before accounting for rate-limit deferrals. Configure the SDK intentionally, cap queue attempts, and set an overall deadline or maximum job age. When that budget is exhausted, move the job to a terminal failure or review state and expose enough error detail for an operator to act.
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Protect the intake endpoint and monitor queue health
Rate limiting the provider-facing worker does not stop abusive or accidental intake from filling storage and queue capacity. Apply per-customer submission limits, payload and duration caps appropriate to the product, authentication, and validation before storing audio. These are workload decisions; no universal values or throughput targets are established by provider documentation.
Monitor queue wait time, oldest job age, attempts per job, 429 frequency, provider latency, and terminal failures. Alert on sustained backlog growth or repeated throttling, and use account/model-specific limits to revise the shared limiter. Record provider request IDs where available so failures can be traced without logging credentials or sensitive audio.
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