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1. Classify the 429 before retrying
A 429 is a status code, not a diagnosis. Inspect the provider’s error body and code, along with relevant response headers, before deciding whether another attempt can help. OpenAI’s guidance distinguishes temporary rate limiting from exhausted prepaid credit and spend or usage limits; those account-level restrictions are not fixed by waiting and retrying. See OpenAI’s 429 troubleshooting guidance.
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- Transient rate or concurrency limit: A later attempt may succeed after capacity becomes available. Apply a finite retry policy.
- Credits or spend/usage limit: Stop automatic retries and surface an account or billing action. Repeating the request does not restore credit or raise a configured limit.
- Hard session-duration limit: A completed session limit requires a new, valid session or a changed request strategy, not repeated retries of the same expired session.
- Unknown or malformed error: Preserve the status, provider error code, and safe response metadata for diagnosis; do not treat every unrecognized 429 as transient.
For Amazon Transcribe streaming, AWS documents LimitExceededException with HTTP 429. It commonly indicates that the concurrent-stream quota was exceeded, but AWS also identifies maximum session duration and rapidly increasing concurrency as possible causes. The recovery differs by cause: reduce concurrent streams for a concurrency limit, while an ended hard-duration session needs a new session. Consult the streaming API reference and streaming guide.
2. Check the quota’s scope, unit, and request mode
Before tuning a Node.js worker pool, write down the provider, API generation, project or region, request mode, and quota dimension. Requests per minute, concurrent streams, processed audio volume, request size, and session duration are not interchangeable. A limit for one mode or API generation may not apply to another.
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| Provider and mode | Interaction and operation type | Quota or limit scope | Queue and concurrency behavior |
|---|---|---|---|
| Google Cloud Speech-to-Text v1 synchronous | Synchronous recognition; the requests overview describes this mode for audio of one minute or less. | The v1 quota page lists 900 recognition requests per 60 seconds and 480 hours of audio processing per day, shared by applications and IP addresses using a developer project. These are Google’s documented v1 values, marked updated 2026-09-30 UTC in the search result; verify the current project quota before relying on them. | Those figures do not establish a provider-managed FIFO queue. Check the applicable quota page and manage admission in the client or service architecture. |
| Google Cloud Speech-to-Text v1 asynchronous/long-running | Long-running recognition; Google’s overview describes audio up to 480 minutes. | Use the applicable generation’s quota page and project/region settings; do not assume the v1 request-rate figure alone describes all limits. | Long-running operation is not a guarantee of a queue that accepts unlimited work. Track submitted operations and application-side backlog. |
| Google Cloud Speech-to-Text streaming | Real-time streaming recognition. | The current quota page presents mode-specific and regional limits, including request, size, or session dimensions. The exact applicable value depends on API generation and region. | Treat stream concurrency and session constraints separately from request-rate quotas; verify the configured region and mode. |
| Amazon Transcribe batch jobs | Asynchronous transcription jobs. | Concurrent processing capacity governs when jobs can run. | Optional provider-managed job queueing defers work beyond the concurrent processing limit and processes jobs FIFO. AWS documents a maximum of 10,000 queued jobs and a default queue processing bandwidth ratio of 0.9; defaults may be increased on request. Values are AWS-specific and can change. |
| Amazon Transcribe streaming | Real-time streaming session. | Concurrent-stream quota and session-duration limits are distinct failure dimensions. | Reduce stream concurrency and use exponential backoff for relevant transient cases; a stream-duration limit is not solved by retrying the same ended session. |
Google documents synchronous, asynchronous/long-running, and streaming modes in its v1 requests overview and current overview. The v1 quota page and current quota page describe different quota contexts; check the one matching the deployed API generation and region rather than combining their numbers.
3. Retry with a finite budget and a single owner
Honor a valid Retry-After value when present. If it is missing or invalid, OpenAI advises exponential backoff with jitter, increasing the delay after unsuccessful attempts and adding a small random delay. Its documentation also warns that unsuccessful attempts count toward per-minute limits, so an unbounded or tightly packed loop can worsen throttling. See OpenAI’s retry guidance.
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Set both a maximum attempt count and a maximum elapsed retry time. Keep a retry budget at the request or job level so a task cannot consume capacity indefinitely. Google’s SLA specifies a back-off requirement for its own SLA context: at least one second after the first error, increasing exponentially up to 32 seconds. That contractual rule applies to that SLA context, not as a universal interval for every speech API. See the Google Cloud Speech-to-Text SLA.
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- Do not nest retry loops accidentally. Official OpenAI SDKs already retry eligible errors and honor
Retry-After. If application code adds another retry wrapper, account for SDK attempts in the total budget or disable one layer. - Do not retry permanent failures. Stop on account, billing, invalid-request, or completed hard-session limits and report an actionable failure.
- Do not turn a timeout into an assumed-safe replay. For job submissions, consider whether the provider accepted the original request before resubmitting. The cited provider documentation does not establish one universal idempotency guarantee across speech APIs.
4. Control admission and separate client queues from provider queues
A local queue should regulate how many jobs are eligible to reach the provider, not simply store work and release every item at once. Set an explicit concurrency ceiling and, where relevant, a request-rate ceiling based on the quota dimension you identified. If many jobs receive 429s together, stagger their eligibility rather than letting all of them retry on the same timer.
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Amazon Transcribe’s optional batch-job queue is provider-managed: it defers jobs that exceed concurrent processing capacity and processes them FIFO, subject to AWS’s documented queue limits. That behavior is specific to Amazon Transcribe job queueing; it is not a guarantee for streaming or a feature to assume for OpenAI or Google Cloud. See Amazon Transcribe job queueing.
For application-managed queues, define what happens when a job reaches its retry budget: mark it failed for operator or user action, dead-letter it, or drop it only if the product’s data-loss policy permits. Track duplicate-submission risk for jobs whose initial request may have succeeded despite a lost response; choose idempotency keys or deduplication only where the provider and operation support them.
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5. Make retries and queue health observable
Recommended application metrics—not vendor-mandated fields—should let an operator distinguish provider throttling from an overloaded local worker pool. Emit, at minimum:
- Provider, API generation, region or project, and request mode.
- HTTP status and provider error code, plus whether the response supplied a usable
Retry-After. - Attempt number, selected delay, retry-budget exhaustion, and final outcome.
- Queue depth, oldest-job age, active concurrency, and enqueue-to-completion time.
- Counts of accepted, deferred, retried, completed, failed, and dead-lettered jobs.
Keep logs useful without recording API credentials, sensitive audio, or transcript contents. Avoid high-cardinality metric labels such as raw job IDs; retain identifiers in appropriately protected logs or traces when needed to investigate one job.
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A Node.js implementation checklist
- Normalize provider responses. Convert status, provider error code, response headers, and request mode into a shared internal error shape while preserving provider-specific details for diagnosis.
- Classify before scheduling. Retry only errors classified as transient. Route account limits and hard session failures to a terminal or remediation path.
- Parse
Retry-Aftersafely. Use it when valid; otherwise calculate exponential backoff with jitter. Clamp waits to the job’s remaining time budget. - Choose one retry owner. Document whether the SDK or application controls retries and count all attempts toward the same finite budget.
- Gate queue admission. Apply mode-appropriate concurrency and rate controls. Do not let a backlog become a synchronized burst when capacity returns.
- Record state transitions. Emit enqueue, attempt, delay, completion, terminal failure, and retry exhaustion so queue age and completion latency can be measured.
- Test provider-specific cases. Exercise transient throttling, account or quota exhaustion, concurrency overload, timeout after possible acceptance, and retry-budget exhaustion without using sensitive production audio.
For Google Cloud Node.js streaming, the official example uses the client library and notes that SoX must be installed and available in PATH; that is a requirement of its sample audio-capture pipeline, not a rate-limit fix. See the Google Node.js streaming example.
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