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Non-Production Azure OpenAI Deployments Using PTUs Instead of PAYG
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Non-Production Azure OpenAI Deployments Using PTUs Instead of PAYG
Ariel Lichterman
CER:
Azure-AI-5981
Service Category
AI
Cloud Provider
Azure
Service Name
Azure Cognitive Services
Inefficiency Type
Misaligned Pricing Model
Explanation

Development, testing, QA, and sandbox environments rarely have the steady, predictable traffic patterns needed to justify PTU deployments. These workloads often run intermittently, with lower throughput and shorter usage windows. When PTUs are assigned to such environments, the fixed hourly billing generates continuous cost with little utilization. Switching non-production workloads to PAYG aligns cost with actual usage and eliminates the overhead of managing PTU quota in low-stakes environments.

Relevant Billing Model

PAYG charges per input and output token, making it cost-efficient for sporadic or low-volume workloads. PTUs incur fixed hourly charges regardless of utilization. Using PTUs in non-production environments typically results in paying for unused capacity.

Detection
  • Identify OpenAI deployments in dev, test, QA, or sandbox environments running on PTUs
  • Review utilization patterns showing intermittent or low-throughput activity
  • Evaluate whether non-production environments require dedicated throughput or low-latency guarantees
  • Check for PTU deployments where performance requirements could be met by PAYG consumption
Remediation
  • Migrate non-production OpenAI deployments from PTUs to PAYG to align cost with usage
  • Create environment-based deployment standards specifying PAYG as the default for non-prod workloads
  • Implement workload reviews to validate whether PTU performance guarantees are truly required
  • Periodically assess non-production usage patterns to prevent unnecessary PTU provisioning
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