Pricing and Packaging for AI Products: A 7-Step Guide

AI product pricing and packaging must balance customer-facing value with highly variable underlying costs driven by models, tokens, compute, agent activity, tools, and external APIs. An effective approach begins by identifying the outcome customers value, analyzing cost drivers, selecting a suitable model such as subscription, usage-based, credits, seats, hybrid, or outcome-based pricing, and translating plans into enforceable entitlements, limits, model access, budgets, and credit rules. Products also need explicit behavior for exhaustion of credits or limits, including blocking requests, allowing overages, top-ups, approvals, or lower-cost alternatives, with real-time enforcement that handles concurrent usage. Common pitfalls include exposing infrastructure metrics directly, creating too many billing units, hard-coding plan logic, confusing metering with enforcement, and accumulating custom exceptions that make pricing changes difficult. Pricing should be revisited when margins diverge, customers cannot predict costs, new models alter economics, or engineering must intervene in routine commercial updates. Stigg is presented as a usage-runtime platform that centralizes product catalogs, credits, entitlements, metering, ledger-backed balances, hierarchical account structures, and synchronous enforcement while integrating with existing billing and revenue systems.

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