27 articles on machine learning — what drives the cost, how it is priced, and where the savings actually are.
Cloud TPU bills per chip-hour by TPU version and configuration, with on-demand, Spot (preemptible), and committed-use pricing. Idle TPUs and full-price on-demand for interruptible training are the expensive mistakes. Here is how to cut cost.
Inf2 (Inferentia) and Trn1 (Trainium) instances are AWS custom ML accelerators priced per instance-hour, often far cheaper per inference or training step than GPU instances. The catch is model compatibility. Here is when they cut cost.
Vertex AI bills training per node-hour, online prediction per node-hour for the endpoint you keep deployed, plus AutoML and model API usage. Idle deployed endpoints are the classic surprise. Here is where the money goes and how to cut it.