Part 8/11:
Initially hosted on AWS, the project faced runaway costs. A single model experiment cost $12,000 within 36 hours, burning through a quarter’s budget and causing chaos among finance and tech teams. The API-driven infrastructure lacked transparency, making it difficult to track resource utilization or control costs.
This example highlights why clear, predictable budgeting tools are critical—especially as enterprises scale their AI initiatives.
Why Granular Cost Control Is Non-Negotiable for Enterprises
The core takeaway is that cost predictability and control are essential. Enterprises need to:
Specify resource requirements (e.g., particular CPUs or GPUs) upfront
Set fixed, flat-rate pricing for inferencing and training endpoints