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Overview
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Learn how to build robust pricing infrastructure for AI products in this conference talk that explores the engineering challenges behind monetizing AI applications. Discover architectural patterns for creating pricing systems that can evolve alongside your AI technology stack, covering essential topics like usage metering, cost mapping, and pricing strategy design. Explore how to handle complex business models including self-serve versus enterprise motions and pay-as-you-go versus committed contracts. Master techniques for safely testing pricing changes and implementing rollback mechanisms when needed. Gain insights into factoring margins and underlying costs into your pricing strategy while maintaining accuracy and auditability in your billing systems. Whether building a pricing system from scratch or upgrading existing infrastructure, acquire the mental models and engineering approaches needed to treat pricing as a first-class product concern that scales with modern AI workloads.
Syllabus
Revenue Engineering: How to Price (and Reprice) Your AI Product — Kshitij Grover, Orb
Taught by
AI Engineer