I've been digging into Azure OpenAI's rate limiting and have noticed some puzzling differences between what's documented and the actual behavior. I've set up my system to process documents from Azure OpenAI's API, using a token limiter that refills 15,000 tokens every 250ms, which should give me about 3.6 million tokens per minute. I also reserve around 11,000 tokens for each API call, even though my actual consumption is around 9,000 tokens. I've implemented a safety buffer of 1,500 tokens to avoid exceeding limits, but here's where it gets tricky: according to Azure's documentation, I should be able to handle 4 million tokens per minute and around 4 requests per second, especially since I'm using an S0 tier deployment.
However, I'm seeing much lower effective limits—less than 20% of what I expected! I'm curious if anyone else has run into this issue. I'm hoping to clarify Azure's rate limiting policy and ideally suggest some improvements to their documentation to reflect what we're actually experiencing. Anyone else notice this discrepancy?
3 Answers
Yeah, the region can definitely impact performance. If you're in the Netherlands using a model based in Switzerland, latency might be a factor too. But it's definitely frustrating when the limits don’t align with what Azure claims.
Have you checked which model you're using for your deployment? Sometimes the limitations can vary based on the model version or location. It's worth looking into that because it might explain some of the unexpected behavior you're seeing.
Exactly! I've noticed similar issues. It seems like the documentation doesn't keep up with the real-world performance sometimes. I'm using the GPT-4o-mini model and I haven’t had much luck either with the stated limits.
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