AI API for SaaS: Ship Smarter Features, Not Bigger Bills

Building an AI API feature for a SaaS product requires measuring useful outcomes rather than simply successful model responses, with support-ticket triage and editable reply drafting presented as a practical example. The recommended approach is to define acceptance, quality, latency, reliability, governance, and cost thresholds; evaluate candidate models on the same de-identified cases; validate structured outputs in the backend; and separate model generation from authorization for any consequential action. Production controls include tenant-scoped access, server-side credentials, idempotent logical jobs, bounded retries, queues, feature flags, human review, cost reservations, and detailed attempt-level logging tied to tenant, feature, model, prompt version, usage, latency, and eventual reviewer acceptance. Costs should include retries, context, tools, storage, and review time, with cost per accepted output serving as a more meaningful measure than cost per request. The guidance recommends starting with one narrowly bounded, measurable feature, using a default model and tested fallback, monitoring acceptance, rewrite rate, P95 end-to-end latency, and cost weekly, and expanding only after real usage demonstrates acceptable quality, safety, and margins.

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