Short answer: compare OpenAI, Claude, and Gemini text classification APIs by migrating one tenant cohort at a time, keeping exactly one path authorized to write CRM actions while every candidate returns metered, validated JSON in shadow.
That is the simple backend design I would trust for comparing OpenAI, Claude, and Gemini. A global bake-off hides the constraint that matters here: a sales-call summarizer needs per-tenant cost visibility across Europe and US processing paths, including the temporary cost of migration. Switching a model name in config is easy. Proving which tenant paid for both paths, and preventing the shadow from creating duplicate CRM work, is the real job.
The unit of change is a tenant cohort, not the whole app.
How should an app backend compare JSON text classification tagging APIs?
The domain output should stay boring. A reviewed call summary can produce schedule_security_review, update_integration_requirement, assign_account_owner, or no action. Those values form a closed vocabulary. Unknown tags and malformed JSON are rejected before any comparison, because two adapters returning equally invalid objects have not demonstrated useful agreement.
