Note
Access to this page requires authorization. You can try signing in or changing directories.
Access to this page requires authorization. You can try changing directories.
This article provides a quick reference and detailed description of the quotas and limits for Azure AI Foundry Models. For quotas and limits specific to the Azure OpenAI in Foundry Models, see Quota and limits in Azure OpenAI.
Quotas and limits reference
Azure uses quotas and limits to prevent budget overruns due to fraud and to honor Azure capacity constraints. Consider these limits as you scale for production workloads. The following sections provide a quick guide to the default quotas and limits that apply to Azure AI model inference service in Azure AI Foundry:
Resource limits
| Limit name | Limit value |
|---|---|
| Azure AI Foundry resources per region per Azure subscription | 100 |
| Max projects per resource | 250 |
| Max deployments per resource | 32 |
Rate limits
The following table lists limits for Foundry Models for the following rates:
- Tokens per minute
- Requests per minute
- Concurrent request
| Models | Tokens per minute | Requests per minute | Concurrent requests |
|---|---|---|---|
| Azure OpenAI models | Varies per model and SKU. See limits for Azure OpenAI. | Varies per model and SKU. See limits for Azure OpenAI. | not applicable |
| - DeepSeek-R1 - DeepSeek-V3-0324 |
5,000,000 | 5,000 | 300 |
| - Llama 3.3 70B Instruct - Llama-4-Maverick-17B-128E-Instruct-FP8 - Grok 3 - Grok 3 mini |
400,000 | 1,000 | 300 |
| - Flux-Pro 1.1 - Flux.1-Kontext Pro |
not applicable | 2 capacity units (6 requests per minute) | not applicable |
| Rest of models | 400,000 | 1,000 | 300 |
To increase your quota:
- For Azure OpenAI, use Azure AI Foundry Service: Request for Quota Increase to submit your request.
- For other models, see request increases to the default limits.
Due to high demand, we evaluate limit increase requests per request.
Other limits
| Limit name | Limit value |
|---|---|
| Max number of custom headers in API requests1 | 10 |
1 Our current APIs allow up to 10 custom headers, which the pipeline passes through and returns. If you exceed this header count, your request results in an HTTP 431 error. To resolve this error, reduce the header volume. Future API versions won't pass through custom headers. We recommend that you don't depend on custom headers in future system architectures.
Usage tiers
Global Standard deployments use Azure's global infrastructure to dynamically route customer traffic to the data center with best availability for the customer's inference requests. This infrastructure enables more consistent latency for customers with low to medium levels of traffic. Customers with high sustained levels of usage might see more variabilities in response latency.
The Usage Limit determines the level of usage above which customers might see larger variability in response latency. A customer's usage is defined per model and is the total tokens consumed across all deployments in all subscriptions in all regions for a given tenant.
Request increases to the default limits
You can submit limit increase requests, which we evaluate one at a time. Open an online customer support request. When you request an endpoint limit increase, provide the following information:
Select Service and subscription limits (quotas) as the Issue type when you open the support request.
Select the subscription you want to use.
Select Cognitive Services as Quota type.
Select Next.
On the Additional details tab, provide detailed reasons for the limit increase so that your request can be processed. Be sure to add the following information to the reason for limit increase:
- Model name, model version (if applicable), and deployment type (SKU).
- Description of your scenario and workload.
- Rationale for the requested increase.
- Target throughput: Tokens per minute, requests per minute, and other relevant metrics.
- Planned time plan (by when you need increased limits).
Select Save and continue.
General best practices to stay within rate limits
To minimize issues related to rate limits, 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.
Next steps
- Learn more about the models available in Azure AI Foundry Models