# Underutilized PTU Quota for Azure OpenAI Deployments

Canonical: https://www.pointfive.co/efficiency-hub/inefficiencies/underutilized-ptu-quota-for-azure-openai-deployments-da831

[Cloud Efficiency Hub](https://www.pointfive.co/efficiency-hub) 

# Underutilized PTU Quota for Azure OpenAI Deployments

## Explanation

When organizations size PTU capacity based on peak expectations or early traffic projections, they often end up with more throughput than regularly required. If real-world usage plateaus below provisioned levels, a portion of the PTU capacity remains idle but still generates full spend each hour. This is especially common shortly after production launch or during adoption of newer GPT-4 class models, where early conservative sizing leads to long-term over-allocation. Rightsizing PTUs based on observed usage patterns ensures that capacity matches actual demand.

## Relevant Billing Model

PTU pricing is based on the number of provisioned throughput units, not actual usage. Underutilized PTUs still incur full hourly charges, making over-allocation a direct source of avoidable cost.

## Detection

- Review PTU deployments for consistently low or flat throughput utilization over representative time periods

- Compare provisioned PTU levels against actual workload demand to identify idle capacity

- Identify deployments sized for initial peak estimates that no longer match steady-state usage

- Evaluate whether recent model or workload changes have altered throughput requirements

## Remediation

- Reduce PTU allocations to align with actual utilization while preserving required performance levels

- Implement recurring rightsizing reviews to adjust PTU levels as workload patterns evolve

- Use workload performance testing to validate that reduced capacity meets latency and throughput goals

- Consider shifting variable or declining workloads from PTUs to PAYG where appropriate

## Relevant Documentation

- [https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/provisioned-throughput](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/provisioned-throughput)

[Submit Feedback](https://hub.pointfive.co/inefficiencies/underutilized-ptu-quota-for-azure-openai-deployments-da831)

## At a glance

Reference

CER-0266

Cloud provider

Azure

Service

Azure Cognitive Services

Category

[AI](https://www.pointfive.co/efficiency-hub/service-category/ai)

Inefficiency type

Overprovisioned Capacity Allocation

## Contributed by

- Ariel Lichterman 

### Ariel Lichterman

Cloud Researcher @ PointFive

[LinkedIn](https://www.linkedin.com/in/ariel-fishman-lichterman-01a096214/)

---
Source: the public page above. Product screenshots and illustrative interfaces are examples, not live customer data.