Moving Half of Our AI Development to Local LLMs — by Splitting Work by Role, Not by Picking the Biggest Model

Uehara, EarthLink Network Co., Ltd. I build and run more than 20 products by myself, with Claude Code at the core of development. This is a field note from that work. On July 30, 2026, on the control panel that runs our in-house AI development, the share of work handled by local LLMs reached 50.3%, with the cloud side at 48.2%. This does not mean our bill was cut in half. It means the split of execution volume — including transcripts — came out to roughly fifty-fifty. [画像: Share of work between local LLMs and the cloud. Absolute usage and account details have been redacted.]（画像は別途ホスティング予定） How I thought about this changed a great deal from where I started. At first, I thought "just put one big model in" At the beginning my idea was simple: load a single 70B-class model onto the large memory of a DGX Spark, and it would make a good commander. Bigger must be smarter — a naive assumption. Measuring on real hardware gave th