# Provisioned Throughput OpenAI Deployment in Non-Production Environments

Canonical: https://www.pointfive.co/efficiency-hub/inefficiencies/provisioned-throughput-openai-deployment-in-non-production-environments-cffc6

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

# Provisioned Throughput OpenAI Deployment in Non-Production Environments

## Explanation

PTU deployments guarantee dedicated throughput and low latency, but they also require paying for reserved capacity at all times. In non-production environments-such as dev, test, QA, or experimentation-usage patterns are typically sporadic and unpredictable. Deploying PTUs in these environments leads to consistent baseline spend without corresponding value. On-demand deployments scale usage cost with actual consumption, making them more cost-efficient for variable workloads.

## Relevant Billing Model

Provisioned Throughput Units are billed at a fixed hourly rate regardless of utilization. They are optimized for steady, high-throughput workloads. Non-production environments with low or inconsistent usage pay for committed capacity they rarely consume, making PTUs significantly more expensive than the on-demand consumption model.

## Detection

- Review OpenAI deployments in non-production environments to determine whether PTUs are configured instead of on-demand

- Assess utilization patterns to see if throughput demand fluctuates or remains low compared to the allocated PTUs

- Confirm whether the environment requires dedicated capacity, or if on-demand latency and throughput are sufficient

- Evaluate overall spend on PTU deployments relative to business value delivered in non-production settings

## Remediation

- Switch non-production OpenAI deployments from PTU to on-demand consumption pricing

- Reserve PTUs only for production workloads with sustained, predictable throughput requirements

- Establish governance standards to ensure deployment models match workload profiles across environments

- Periodically review OpenAI usage patterns to validate that non-production capacity aligns with actual utilization

## 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/provisioned-throughput-openai-deployment-in-non-production-environments-cffc6)

## At a glance

Reference

CER-0252

Cloud provider

Azure

Service

Azure Cognitive Services

Category

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

Inefficiency type

Overprovisioned Deployment Model

## Contributed by

- Ariel Lichterman 

### Ariel Lichterman

Cloud Researcher @ PointFive

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

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