Most tokens burned by an AI coding agent are spent on I/O and boilerplate, not reasoning. Here’s how to slash your bill by routing grunt work to a cheaper model while keeping your frontier model for the problems that actually need it.
GPUs are no longer a hidden infrastructure cost. They are a tracked business metric, tied to revenue, margins, and team accountability. This post unpacks why utilization moved from engineering dashboards to board decks, and what it means for the way you build, schedule, and bill ML workloads.
How to design consolidation disruption policies in Karpenter that aggressively reclaim underutilized nodes without thrashing your Spot workloads, with real EKS configuration examples.