Model Migration

Model migration is the planned move from one AI model or model version to another — for example when a provider retires an existing model, a stronger version ships, or cost, latency, or compliance requirements change. Unlike an automatic fallback that only kicks in during an outage, migration is a deliberately orchestrated project with a test phase, side-by-side measurement, and a fixed cut-over date. A typical migration starts by inventorying every place the old model is called, then evaluates the new model in parallel against real prompts and quality criteria, adjusts system prompts and parameters, and finally switches over in a controlled way — often gradually through feature flags or a canary share of traffic. Because models behave differently, swapping the model name is rarely enough on its own: tone, formatting, tool calls, and the cost profile all have to be re-verified before and after the change. A well-planned migration keeps deprecation deadlines from turning into frantic last-minute scrambles and ensures an application's quality and behavior stay stable across the switch.