Build a durable queue, then enforce the provider’s request limit across every worker that shares its quota. In Node.js, BullMQ can pace jobs globally for a queue and can return a throttled job to the waiting state when a provider responds with a temporary 429. The design below covers completed audio files; live audio streams need a real-time transcription flow instead.
Choose the right transcription flow
A queue is a good fit for recordings that are complete before transcription begins: a user uploads a file, your application stores it, and a worker submits it when capacity is available. It decouples client response time from provider latency and gives you a place to control retries.
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For OpenAI’s file transcription flow, the documented endpoint is /v1/audio/transcriptions. The current guide specifies a 25 MB maximum file size and lists supported formats; check that guide for the current format list and any model-specific requirements before accepting uploads. These are OpenAI-specific constraints, not universal limits for speech-to-text APIs. OpenAI speech-to-text guide.
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Design the job lifecycle before writing the worker
Accept a reference, not a blob
Validate file type and size at intake, store the audio in durable object or file storage, and put its reference plus the minimum required metadata in the queue. Avoid putting large raw audio or provider credentials in ordinary job data. Keep credentials on the server, accessible only to the worker that calls the provider.
Make submission idempotent
A client may time out after the server has enqueued a job but before it receives the response. If it retries, blindly creating another job can produce a second transcription and another charge. Accept a stable client request key or create a durable submission record, enforce uniqueness for the relevant customer and request, and return the existing job identifier when the same request is submitted again. Persist the mapping so the protection survives process restarts.
Persist the result and state transitions
Record a job identifier, audio reference, customer or tenant identifier, status, attempt count, timestamps, and eventual result location. Save the provider request ID when the API returns one. Make result persistence safe to repeat: a worker can crash after the provider finishes but before it acknowledges the queue job, so the next attempt must not create inconsistent application state.
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Set a limit that covers all workers sharing the quota
Worker count is not a rate-control strategy. If each process independently sends requests at a chosen rate, adding processes can multiply the aggregate traffic. The control must coordinate the work sent by all workers that use the same provider quota.
BullMQ documents a worker limiter that is global across workers processing the same queue: for example, its documentation says that ten workers using a setting of ten jobs per second still process only ten jobs per second in aggregate. Configure the limiter on the queue’s workers using the limit and duration appropriate to the provider account and model. See BullMQ rate limiting and BullMQ global rate limit.
OpenAI does not publish one universal throughput figure for every user. Limits vary by model and organization or project. Use the values applicable to the account and model, and where available inspect the response headers and account dashboard rather than copying a rate from an unrelated example. OpenAI rate limits.
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Use a shared queue and limiter scope that matches the quota being protected. If different queues or independent limiter instances can call the same account, a limit on just one queue may not constrain their combined traffic. BullMQ removed group-key rate limiting in version 3.0, so do not build a new design around older examples that depend on group keys. QueueScheduler is not needed for rate limiting from BullMQ 2.0 onward. Verify behavior against the BullMQ version you install. BullMQ rate limiting.
Static pacing or a response-driven pause?
| Approach | How it works | Best fit and trade-off |
|---|---|---|
| Configured queue limiter | Sets a steady maximum job rate for the shared queue. | Simple when the relevant provider limit is known. It may be conservative or too aggressive if account limits change. |
| Manual rate limiting after a 429 | Pauses queue processing in response to provider throttling, then returns the affected job to waiting using BullMQ’s special rate-limit error flow. | Can react to a temporary provider signal such as Retry-After. A broad queue pause can also affect jobs beyond the model or account scope that triggered the response. |
BullMQ documents both configured limiting and manual rate limiting. Choose a scope that corresponds to the quota: if one model or tenant has a distinct limit, avoid pausing unrelated work unless they truly share the same constrained budget. BullMQ rate limiting.
Implement the worker without multiplying retries
The worker’s core sequence is: load the stored audio, call the provider’s transcription endpoint, persist the transcript and provider request ID, then acknowledge successful completion. On failure, classify the response before deciding whether to retry. Keep the provider adapter separate from queue logic so another provider’s formats, errors, or retry semantics do not leak into the BullMQ policy.
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For OpenAI, the official Node.js SDK repository documents two default retries for eligible errors, including 429 responses. That behavior is tied to the SDK documentation on its current repository branch and can vary by installed package version; confirm and configure the behavior for your version. If the SDK retries twice and BullMQ also retries the job, the provider may see several calls for one logical job. Set one intentional policy across both layers, either disabling or limiting one layer where appropriate, and bound total attempts or elapsed time. OpenAI Node.js SDK.
When a retry budget is exhausted, move the job to a terminal failure or review state and retain enough error detail to diagnose it. Do not retry indefinitely: persistent failures can keep consuming quota and prevent newer work from being processed.
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A 429 status alone is not enough to decide what happens next. A temporary rate throttle can be retried after the stated wait; an account quota or billing problem needs account action, not an automatic retry loop. OpenAI’s rate-limit guidance describes rate limits and response headers, while its troubleshooting guidance distinguishes rate-limit and quota issues. OpenAI rate limits and OpenAI 429 troubleshooting.
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- Inspect the response. Record the status, provider error details, relevant rate-limit headers, and request ID when available. Determine whether the failure is temporary throttling or an account/quota condition.
- For a valid Retry-After, wait at least that long. Do not retry sooner than the provider’s requested delay. If the response indicates quota or billing action, stop automatic retries and surface the failure for account remediation.
- If Retry-After is absent or unusable, use bounded exponential backoff with jitter. Increase the delay after repeated temporary failures, cap it, and apply a maximum attempt count or job deadline. Jitter spreads retries from jobs that failed together instead of releasing them as a synchronized burst.
- With BullMQ manual limiting, return the job to waiting through its rate-limit flow. Apply the delay and raise BullMQ’s special rate-limit error rather than treating the event as an ordinary failed job. Follow the current BullMQ API for the installed version. BullMQ rate limiting.
Validate and protect the intake endpoint
- Check the selected provider’s current file-size and format rules before enqueueing; for OpenAI file transcription, the cited guide’s maximum is 25 MB.
- Authenticate callers and enforce per-customer intake limits so one client cannot consume the entire provider budget.
- Set payload caps and reject unsupported or oversized audio before it occupies durable storage and queue capacity.
- Use idempotency keys or equivalent durable deduplication for client retries.
- Keep audio access, queue access, and provider credentials restricted to the services that need them; apply retention rules appropriate to your product and obligations.
Monitor queue health and recovery
Measure queue wait time, job age, attempts per job, provider latency, 429 frequency, and terminal failures. These signals help distinguish a normal backlog from a limiter that is too restrictive, a provider-side throttle, or a class of jobs that will never succeed. Alert on sustained growth or repeated terminal failures, but set thresholds from your own workload and service objectives: the cited documentation does not establish a universal throughput target or latency SLO.
Keep enough operational data to retry or inspect a failed job without exposing raw audio or secrets in logs. A manual review state is useful for persistent provider errors, malformed files that passed intake, or failures that require a customer to upload a corrected recording.
Quick Recap
Practical choices to settle before launch
- Quota scope: Identify which organization, project, model, and queues share the upstream allowance; make the limiter cover that aggregate.
- Retry ownership: Decide how SDK retries and queue retries combine, then set an explicit total-attempt or deadline budget.
- Failure handling: Separate temporary throttles from quota or billing problems and define when jobs become terminal.
- Work type: Queue completed recordings; use a real-time transcription flow for audio that is still arriving.
- Version checks: Confirm the installed BullMQ and OpenAI SDK versions, since documented behavior and defaults can change.
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