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