Your AI product may be generating value your pricing model will never capture.

Executive Summary:

Most B2B AI companies are leaving significant revenue on the table — not because their technology is weak, but because they are using 1990s pricing logic to monetize a 2025 product. Static per-seat subscriptions and cost-plus models were designed for software with fixed hosting costs and predictable value delivery. They break completely when applied to AI products with variable compute costs, compounding model performance, and value that grows every time a customer uses the platform.

This guide provides a complete framework for building, testing, and deploying a pricing strategy purpose-built for AI products. Drawing on C2B Suite's proprietary LLM workflows and the real-world results of our gainshare engagement with Surefire Local — which delivered over $400,000 in annual savings within six months on a 100% outcome-based model — it shows exactly how modern AI pricing gets done.

What's inside:

• A clear-eyed comparison of legacy manual pricing approaches versus C2B Suite's LLM-accelerated model — across speed, cost, precision, and scenario testing

• Why cost-plus, competitor-based, and static value-based models all fail for AI products, and what to use instead

• A four-phase pricing framework: Assess → Identify → Develop & Test → Deploy & Monitor

• A side-by-side comparison of four AI pricing architectures: Usage-Based, Hybrid Subscription, Outcome-Based/Gainshare, and Tiered Value Packages

• The Surefire Local gainshare case study: how C2B Suite structured a zero-risk, results-only engagement that transformed a cost center into a $400K+ annual savings engine

• The four KPIs every AI pricing model must track: ARPU, Gross Margin on Compute, CLTV, and Conversion Rate

Best for: CPOs, CTOs, VPs of Product, and CEOs at B2B SaaS companies investing in AI-native or AI-augmented products.