Quotas and limitations of Azure OpenAi gpt-4o

Question

Good Day,

 

Regarding the quotation and limitations of concurrency tokens

For example gpt-4o 450k TPM

Is that for single region or the entire subscriptions, 

by otherwards, are regions accumulated, as it appeared separated on dashboard

Answer 1

For Azure OpenAI:

• Quotas are applied per deployment (per model per resource).
• If you have multiple regions, each region’s quota is tracked separately in the Azure OpenAI dashboard.
• So quotas are not accumulated across regions. For example:
  • Region A: GPT-4o has 450k TPM
  • Region B: GPT-4o has 450k TPM
  • Your subscription in total does not get 900k TPM — each region is isolated.

In regard to concurrency (max parallel requests)

• Like TPM, concurrency limits are per deployment.
• Concurrency limits shown in the quota dashboard are per model per region.
• So if you need higher concurrency or TPM, you may spread load across regions by deploying the model separately in each.

For more details, refer to https://learn.microsoft.com/en-us/azure/ai-services/openai/quotas-limits?tabs=REST

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hth

Marcin

Answer 2

Hello @Mohamed Hussein,

Yes, you are absolutely right that quotas for Azure OpenAI, including GPT-4o, are applied per model, per deployment, and per region. To build further on this:

Quotas Are Not Aggregated Across Regions

Each Azure region manages its own token-per-minute (TPM) and request concurrency limits independently. For example, deploying GPT-4o in East US and West Europe will give you 450K TPM in each region, but they remain isolated and do not combine into a 900K TPM quota across your subscription. This separation is intentional and designed to offer predictable regional capacity.

Concurrency Limits Are Also Per Deployment

Concurrency refers to the number of parallel requests that can be processed. These limits are also region- and model-specific. If you're facing throttling, consider distributing traffic across multiple deployments of the same model in different regions, which is an effective way to scale horizontally without waiting for a quota increase.

Quotas Are Tracked Per Resource, Not Subscription-Wide

Even if your deployments are in the same region, quota enforcement is per Azure OpenAI resource. So deploying the same model under two separate resources in the same region doesn't combine the quota. This enables multi-tenant or multi-project isolation, but requires careful planning for high-throughput applications.

Monitoring Quotas and Usage Use the Azure portal > Azure OpenAI Resource > Quotas blade to view your TPM and concurrency limits. For deeper insight, enable Azure Monitor and Application Insights to track real-time usage, throttling, and latency.

To scale beyond default Azure OpenAI quotas, you can deploy the same model across multiple regions to distribute traffic, since quotas are region-specific. Batching requests and optimizing prompt length can reduce token usage and help stay within TPM limits. If higher usage is expected, submit a quota increase request with a clear business justification and projected growth. For more consistent performance, consider using Dedicated Capacity SKUs if available for your workload.

Please Refer this Azure OpenAI Service Quotas and Limits

I Hope this helps. Do let me know if you have any further queries.

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