Developer's Test Shows Server Queue Time, Not AI Model Speed, Slows Refactoring

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A developer spent two weeks measuring the four phases of an AI-assisted code refactoring workflow — prompt serialization, server wait, token streaming, and diff application — to identify where time was actually lost. Using a custom bash script with curl timestamps and git apply timing, they compared two network paths on the MonkeyCode platform: a hosted endpoint and a free self-hosted server option. The hosted path took 42.8 seconds before the first token arrived, while the free server delivered it in just 5.1 seconds, cutting total refactor time from 111 seconds to 56 seconds. The data showed that the bottleneck was server queue latency, not model inference speed, as local diff application times were nearly identical across both paths. The experiment highlights that time-to-first-token, rather than raw tokens-per-second, is what most affects a developer's perceived responsiveness during AI-assisted coding.

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