Deploying, Operating & Selling AI Products

Getting an AI feature working on your laptop is the easy part. Running it in production means dealing with model calls that take minutes instead of milliseconds, streams that pass through proxies and load balancers, providers that rate-limit or go down, and a bill that grows with every request. Selling it means pricing a product whose costs rise with usage, explaining why it is more than a thin layer over someone else's model, and charging customers accurately for what they use.

This module covers both halves. On the operations side, you will design an architecture where every model call goes through one service layer, choose hosting that fits long-running and streaming AI routes, configure timeouts so the right layer fails first, and use durable workflows for multi-step jobs that run for hours or wait days for human review. You will log and trace every model call with tokens, latency, and cost, ship prompt and model changes behind feature flags with shadow tests and instant rollback, and prepare for provider outages, quality regressions, and runaway spend with degraded modes and incident runbooks.

On the business side, you will compare pricing models for products with real per-use costs, including seats, usage-based pricing, credits, and hybrids, and choose one with margins in mind. You will learn where defensibility actually comes from (workflow, data, distribution, and quality) and how to position your product so buyers see it. Then you will meter usage per customer, enforce plan limits, and bill with Stripe, including credits and overages, and reconcile it against your provider invoice.

The module ends with launching and iterating: getting your first users and running a weekly loop of usage data, error analysis, and roadmap decisions. Examples use TypeScript, Next.js, Postgres, and the Anthropic SDK, built around a help-desk app, but the patterns apply to any stack and any model provider.