Deployment & Scaling · Advanced
Cost and Scale on Azure AI: Quota, PTU, Batch and Monitoring
Where the money actually goes on an Azure AI workload, how provisioned throughput changes the economics at scale, and the monitoring that stops a pilot becoming an invoice.
- 45 min lesson
- intermediate level
- Azure AI Track
- Narrated presentation
What you’ll learn
- Four cost centres: inference, retrieval, ancillary services, hosting compute
- Standard per-token versus provisioned throughput hourly reservations, and the utilisation crossover
- Model right-sizing per task as the largest single saving
- Batch processing for non-interactive work and prompt caching for repeated prefixes
- Capping output length and shrinking retrieved context as direct cost levers
- AI Search replicas versus partitions and the habit of over-provisioning both
- Cost per successful task as the executive metric, not cost per token
Inside this lesson
A narrated, slide-by-slide presentation with 1 prediction exercise, 1 knowledge check, 3 videos. You answer as you go, and the platform checks your work.