Azure OpenAI Service quotas and limits

This article contains a quick reference and a detailed description of the quotas and limits for Azure OpenAI in Azure AI services.

Quotas and limits reference

The following sections provide you with a quick guide to the default quotas and limits that apply to Azure OpenAI:

Limit Name Limit Value
OpenAI resources per region per Azure subscription 30
Default DALL-E 2 quota limits 2 concurrent requests
Default DALL-E 3 quota limits 2 capacity units (6 requests per minute)
Maximum prompt tokens per request Varies per model. For more information, see Azure OpenAI Service models
Max fine-tuned model deployments 5
Total number of training jobs per resource 100
Max simultaneous running training jobs per resource 1
Max training jobs queued 20
Max Files per resource (fine-tuning) 30
Total size of all files per resource (fine-tuning) 1 GB
Max training job time (job will fail if exceeded) 720 hours
Max training job size (tokens in training file) x (# of epochs) 2 Billion
Max size of all files per upload (Azure OpenAI on your data) 16 MB
Max number or inputs in array with /embeddings 2048
Max number of /chat/completions messages 2048
Max number of /chat/completions functions 128
Max number of /chat completions tools 128
Maximum number of Provisioned throughput units per deployment 100,000
Max files per Assistant/thread 20
Max file size for Assistants & fine-tuning 512 MB
Assistants token limit 2,000,000 token limit

Regional quota limits

The default quota for models varies by model and region. Default quota limits are subject to change.

Quota for standard deployments is described in of terms of Tokens-Per-Minute (TPM).

Region GPT-4 GPT-4-32K GPT-4-Turbo GPT-4-Turbo-V GPT-35-Turbo GPT-35-Turbo-Instruct Text-Embedding-Ada-002 text-embedding-3-small text-embedding-3-large Babbage-002 Babbage-002 - finetune Davinci-002 Davinci-002 - finetune GPT-35-Turbo - finetune GPT-35-Turbo-1106 - finetune GPT-35-Turbo-0125 - finetune
australiaeast 40 K 80 K 80 K 30 K 300 K - 350 K - - - - - - - - -
brazilsouth - - - - - - 350 K - - - - - - - - -
canadaeast 40 K 80 K 80 K - 300 K - 350 K 350 K 350 K - - - - - - -
eastus - - 80 K - 240 K 240 K 240 K 350 K 350 K - - - - - - -
eastus2 - 80 K 80 K - 300 K - 350 K 350 K 350 K - - - - 250 K 250 K 250 K
francecentral 20 K 60 K 80 K - 240 K - 240 K - - - - - - - - -
japaneast - - - 30 K 300 K - 350 K - - - - - - - - -
northcentralus - - 80 K - 300 K - 350 K - - 240 K 250 K 240 K 250 K 250 K 250 K 250 K
norwayeast - - 150 K - - - 350 K - - - - - - - - -
southafricanorth - - - - - - 350 K - - - - - - - - -
southcentralus - - 80 K - 240 K - 240 K - - - - - - - - -
southindia - - 150 K - 300 K - 350 K - - - - - - - - -
swedencentral 40 K 80 K 150 K 30 K 300 K 240 K 350 K - - 240 K 250 K 240 K 250 K 250 K 250 K 250 K
switzerlandnorth 40 K 80 K - 30 K 300 K - 350 K - - - - - - - - -
uksouth - - 80 K - 240 K - 350 K - - - - - - - - -
westeurope - - - - 240 K - 240 K - - - - - - - - -
westus - - 80 K 30 K 300 K - 350 K - - - - - - - - -

1 K = 1000 Tokens-Per-Minute (TPM). The relationship between TPM and Requests Per Minute (RPM) is currently defined as 6 RPM per 1000 TPM.

General best practices to remain within rate limits

To minimize issues related to rate limits, it's a good idea to use the following techniques:

  • Implement retry logic in your application.
  • Avoid sharp changes in the workload. Increase the workload gradually.
  • Test different load increase patterns.
  • Increase the quota assigned to your deployment. Move quota from another deployment, if necessary.

How to request increases to the default quotas and limits

Quota increase requests can be submitted from the Quotas page of Azure OpenAI Studio. Please note that due to overwhelming demand, quota increase requests are being accepted and will be filled in the order they are received. Priority will be given to customers who generate traffic that consumes the existing quota allocation, and your request may be denied if this condition isn't met.

For other rate limits, please submit a service request.

Next steps

Explore how to manage quota for your Azure OpenAI deployments. Learn more about the underlying models that power Azure OpenAI.