Retry failed requests with exponential backoff and reduce request concurrency.
Updated
Verification status
General troubleshooting guidance
Evidence
0 public source URLs
Before you change production
This page does not expose source URLs in the page body. Treat it as general troubleshooting guidance and verify against official documentation before changing systems.
Reproduce the smallest failing action and save non-secret logs before changing configuration.
Check versions for OpenAI API, related SDKs, package managers, CI runners, and hosting providers.
Change one setting or dependency at a time, then rerun the same failing command or request.
Avoid destructive commands, credential rotation, billing changes, or security relaxations without a rollback plan.
What this error means
An OpenAI API rate limit error means the API rejected the request because the project, model, or account exceeded an allowed request or token rate.
Common causes
A batch job sends too many requests at once.
The prompt or response size consumes more tokens than expected.
Retry logic immediately repeats failed requests and increases traffic.
Your project has a lower limit than the workload requires.
Quick fixes
Add exponential backoff with jitter for retryable 429 responses.
Lower concurrency for batch jobs and workers.
Reduce prompt size or requested output length.
Check project limits and billing status in the OpenAI dashboard.
Step-by-step troubleshooting
Log response status, request IDs, model names, and retry timing.
Separate request-per-minute issues from token-per-minute issues.
Queue requests so workers do not all retry at the same instant.
Cache repeated results where possible.
If the workload is legitimate and optimized, request a limit increase from the provider dashboard.
Diagnostic flow for this page
Match RateLimitError: 429 Too Many Requests exactly before applying the quick fix.
Compare the failing environment with OpenAI API versions, account scope, provider settings, and deployment context.
Check the listed common causes in order, starting with the cause that best matches your logs.
Use the evidence status below to decide whether to confirm against public sources or official documentation.
Apply one reversible change, rerun the smallest failing action, and keep rollback notes.