The assumption behind scalable SaaS pricing is increasingly under pressure.
Traditional software businesses benefit from a remarkably attractive cost structure: after development, serving an additional user often adds relatively little expense. Subscription revenue grows while marginal delivery cost remains low.
AI-powered software complicates that logic. Inference consumes computation each time a customer interacts with the product. Output length, context size, model selection and repeated calls all affect the cost of serving demand.
An AI feature can become more popular while its gross margin gets worse. High-usage customers may generate exactly the workload that makes an otherwise successful product difficult to monetize.
This does not mean AI products are unattractive. It means they need a clearer model of cost per useful outcome, a pricing metric aligned with value, and controls over workloads that can grow unexpectedly.
Pricing and product architecture can no longer be treated as separate decisions. The economics of tokens, routing, context and agent loops directly influence the business model.
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