#OpenAi Rate Limit per minute

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surreal condor
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I am trying to use GPT 4 azure open ai in my application which is used by multiple users, now it gets rate limited in between of that one minute and all others need to wait as well , what can i do about it

mighty solsticeBOT
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Hi @surreal condor. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

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OpenAI imposes rate limits on its API usage to ensure fair access to its resources. The rate limits are defined in terms of requests per minute (RPM) and tokens per minute (TPM). If you're hitting the limit on requests per minute, there are a few strategies you can employ to optimize your usage:

  1. Increase your usage tier: If you're on a lower usage tier, you might be facing stricter rate limits. By increasing your usage tier, you can get better rates. For example, if you pay at least $5, you can get to usage tier 1 with better rates, in this case, 500 RPM (source (https://community.openai.com/t/566911)).

  2. Batching requests: If your use case does not require immediate responses, you can use the Batch API to more easily submit and execute large collections of requests without impacting your synchronous request rate limits. If you're hitting the limit on requests per minute but have available capacity on tokens per minute, you can increase your throughput by batching multiple tasks into each request. This will allow you to process more tokens per minute, especially with smaller models (source (https://platform.openai.com/docs/guides/rate-limits)).

  3. Exponential backoff: If you're hitting rate limit errors, you can handle these errors using exponential backoff. This means performing a short sleep when a rate limit error is hit, then retrying the unsuccessful request. If the request is still unsuccessful, the sleep length is increased and the process is repeated. This continues until the request is successful or until a maximum number of retries is reached (source (https://help.openai.com/en/articles/5955604-how-can-i-solve-429-too-many-requests-errors)).

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  1. Consider using OpenAI on Azure: If you're still facing issues, you might want to consider using OpenAI on Azure. However, you should check the signup process and approval requirements for this option (source (https://community.openai.com/t/578276)).

Remember to always match responses back to prompts using the index field when batching requests, as the response object may not return completions in the order of the prompts (source (https://cookbook.openai.com/examples/how_to_handle_rate_limits#batching-requests)).

Please note that these strategies are based on the information available in the provided sources and may not fully resolve your issue. If you continue to face problems, you might want to reach out to OpenAI support for more specific guidance.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

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@surreal condor kapa.ai is still learning and improving, please let us know how it did by reacting below

surreal condor
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i am hitting TPM

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@mighty solstice i am hittin TPM

mighty solsticeBOT
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Let me check the knowledge base for you. One moment! :books:

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If you're hitting the Tokens Per Minute (TPM) limit, it means you're sending too many tokens to the API within a one-minute timeframe. Here are a few strategies you can consider:

  1. Optimize your token usage: Each request you make to the API consumes a certain number of tokens. If you're hitting the TPM limit, you might need to reduce the number of tokens you're using per request. This could involve shortening the text you're sending to the API or reducing the number of requests you're making (source (https://platform.openai.com/docs/guides/rate-limits)).

  2. Use the Batch API: If your use case allows, you can use the Batch API to send multiple tasks in a single request. This can help you optimize your token usage and potentially avoid hitting the TPM limit (source (https://platform.openai.com/docs/guides/rate-limits)).

  3. Increase your usage tier: If you're on a lower usage tier, you might be facing stricter rate limits. By increasing your usage tier, you can get better rates. However, this might not be applicable for all users and you should check with OpenAI's support for more details (source (https://community.openai.com/t/627341)).

  4. Use different models for different tasks: If you're using a single model for all tasks, you might be consuming more tokens than necessary. Consider using different models for different tasks, as some models might consume fewer tokens for the same task (source (https://community.openai.com/t/712193)).

  5. Fine-tuning: If you're using a fine-tuned model, remember that it shares the same rate limit as the base model. So, if you're hitting the TPM limit with the base model, you might also hit it with the fine-tuned model (source (https://platform.openai.com/docs/guides/fine-tuning)).

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Remember, these strategies are based on the information available in the provided sources and may not fully resolve your issue. If you continue to face problems, you might want to reach out to OpenAI support for more specific guidance.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon: