The rapidly growing interest in AI has raised a corresponding demand for specialized cloud compute that is built to run training and inference workloads in a cost-efficient and performant manner. Google Cloud Tensor Processing Units (TPUs) have become a popular accelerated compute solution for AI/ML workloads.

Many companies migrate to the cloud and overlook costs in favor of innovation, speed, and flexibility. They assume that the cloud is inherently more cost-effective than on-premises infrastructure. However, the organizations soon realize that the same characteristics that make the cloud such an enticing and flexible resource can also lead to unexpectedly higher usage bills than expected. The challenge is to find the right balance between optimal system performance, engineering velocity, and cost.