Changing an AI model reaches beyond the API call
Model selection, stored settings and cost calculations need to agree after a migration.
Model-maintenance work required removing obsolete selections while keeping existing application settings and pricing behavior consistent.
Find every model reference
The change covered allowed selections, UI defaults, stored settings, pricing records, cost normalization, documentation and tests. Updating only the request would have left other parts of the application describing a different configuration.
Migrate existing state deliberately
A data migration updated persisted model settings and removed obsolete pricing records. The rollback path accounted for restoring those records. Existing users need a defined transition as much as new installations need a sensible default.
Check fallback behavior
Cost normalization needed to recognize removed models before applying generic name normalization. Otherwise, a broadly matching rule could hide a case that required explicit handling.
What was verified
The migration was applied in the test environment, and targeted tests and project checks passed. These checks support implementation correctness; they are not a claim about a customer’s current deployment or achieved cost savings.
This anonymized note describes engineering experience. Client identities and internal implementation details are omitted.
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