Cloud FinOps was built as a bridge between engineering and finance to prevent “bill shock.” In this short clip, Tarun Batra explains how Cloud FinOps matured, and why AI workloads are forcing FinOps to evolve again.
As AI scales, teams are dealing with GPU clusters, training jobs, and inference spend, plus new metrics like cost per outcome, cost per token, and cost per experiment. Many organizations are now having a hard time predicting AI cost.
If you are thinking about modern FinOps and cloud cost control, explore Gruve’s FinOps Cloud Cost Agent page.