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Why Does My Azure OpenAI Deployment Occasionally Return a '429 Too Many Requests' Error Even When I Am Under the Documented Rate Limits, How to solve?
I am using the Python SDK to access Azure OpenAI (GPT-4o / GPT-4o-mini).
My usage logs show that I'm well below the Tokens-per-Minute and Requests-per-Minute limits for my instance.
Even so, I sometimes get:
429 Too Many Requests Please try again later.
This happens randomly in small batches of requests, even when exponential backoff is turned on.
I checked:
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No other deployments are using the same quota.
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No spikes in use.
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No quota exhaustion in the Azure portal.
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No signs of problems with the model starting up cold.
Some people online say this can happen because of regional load, hidden rate limits, or shared backend capacity, but no one really knows why.
Has anyone looked into this in depth or found a solution that works?
Is this a problem with Azure, or is there something developers need to set up differently?
1 answer
This isn’t you hitting the published limits. Azure OpenAI has extra throttles (burst, short-window, and regional load) that can still give you a 429 error even when your RPM/TPM looks fine.
What really helps is to limit the number of clients that can connect at once, not just the total number of requests.
add jitter to retries so they don’t re-sync smooth traffic instead of sending bursts queue work and drain steadily if it’s critical, run a second Azure OpenAI deployment in another region and fail over treat 429 as normal backpressure from Azure OpenAI, not an error you can fully eliminate.
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