All Posts

AI products have material unit costs —compute, inference, data, human-in-the-loop—that must be tracked and managed from day one. Unlike SaaS, where serving one more customer costs virtually nothing, every AI query incurs a non-trivial expense. Key guardrails include: Granular usage monitoring : Track per-customer usage patterns, token consumption, and resource allocation to identify margin threats early. Cost attribution : Attribute costs to specific features, modules, or cus

From APIs to AI—Why Commercial Architecture Matters The AI technology landscape is evolving rapidly, and the lessons learned from productizing APIs are more relevant than ever as we move into the era of AI. Just as APIs required a structured portfolio architecture and commercial governance to unlock monetization, AI solutions—whether models, agents, RPAs, or autonomous capabilities—demand a similar approach. The fundamentals of commercial architecture are not just helpful; th

TLDR: Dynamic packaging- not dynamic pricing , is the foundation for AI monetization AI is rewriting the rules of software economics, value creation, and customer engagement. Dynamic pricing alone is not enough. Product leaders must embrace dynamic packaging —the modular, outcome-driven assembly of AI components, features, data, and technology assets—anchored in a robust commercial architecture that defines value metrics, monetization rules, and governance from the outset. D

Software-as-a-Service (SaaS) pricing is built on four key fundamentals: product structure (value alignment), portfolio structure (customer alignment), pricing structure (metrics alignment), and cost structure (margin alignment). However, the rise of AI-driven platforms is fundamentally challenging these pillars. Here we examine how AI disrupts each pricing fundamental, with real-world examples. Value alignment: Customers continue to pay as they continue to benefit Traditional

Why Traditional SaaS Pricing Models Fall Short and How Commercial Architecture Must Lead for AI Success AI pricing is one of the toughest puzzles facing the software industry today. It’s not just that AI breaks all four pillars of SaaS pricing —it’s the deeper, architectural struggle at the heart of the problem. Specifically, the challenge lies in making the commercial architecture of a multi-product, AI-powered platform align with its existing portfolio and technical archite

AI pricing is not a tweak to existing SaaS models; it’s a structural problem that demands a new commercial architecture. AI breaks the four fundamentals of SaaS pricing — value, customer, metric, and margin alignment —and exposes an architectural mismatch: teams typically design AI on top of a technical architecture that was built for different value flows. The result is pricing that doesn’t map to outcomes, metrics that don’t reflect customer willingness to pay, and revenue