In this episode, host Michael Bernzweig talks with Mitchell Jones, founder and CEO of Lava, about how to monetize AI features without destroying SaaS margins. Mitchell breaks down why AI introduces real variable costs, why seat‑based pricing no longer reflects value when agents can replace dozens of users, and how usage‑based models can better align price with outcomes. You’ll learn how an AI gateway plus billing stack can track per‑request cost in real time, enforce target margins automatically, and let agents both collect and spend funds so AI‑native products can scale profitably.
How traditional SaaS revenue models are breaking due to AI's variable costs
Layering usage-based pricing on top of subscriptions for better margins
Building real-time cost analytics and margin-aware billing with Lava's tools
The role of an AI gateway in routing calls and managing payments effortlessly
Strategies for startups to rapidly implement hybrid pricing models
Connecting AI call routing with revenue collection in a unified stack
Practical steps for organizations to upgrade their payment systems in an AI era
Tactics for multinational companies to support nearshore AI and engineering teams
Maintaining workflow alignment with AI adoption to avoid siloed inefficiencies
Reframing product offerings in response to AI-driven service shifts
00:00 – Introduction to Lava and AI's impact on SaaS revenue
00:30 – Mitchell’s entrepreneurial journey through COVID and YC
01:56 – Challenges with traditional SaaS models in the AI era
02:25 – The need for new pricing strategies reflecting AI’s variable costs
03:23 – How Lava enables layered usage-based pricing seamlessly
04:13 – Building margin-aware billing with real-time cost analytics
07:17 – Importance of partnerships with developer tools and AI coding agents
08:16 – Mitchell’s background with payments and AI monetization
09:15 – AI introduces variable costs that threaten margins
10:41 – Implementing value-aligned pricing models
11:44 – The $80 billion payments opportunity through AI
12:42 – Lava’s API for multi-model AI access and real-time analytics
13:11 – Charging customers based on actual AI usage with margin controls
14:26 – Routing AI calls and payments in one unified gateway
15:23 – Measuring costs and revenue for profitable AI monetization
16:16 – Overcoming organizational challenges with AI workflows
17:28 – Transitioning from fixed seat-based to hybrid pricing models
19:12 – Building efficient, real-time revenue recognition systems
20:04 – Typical enterprise payments stack and its limitations
21:32 – Simplifying complex billing and revenue recognition with Lava
22:08 – Implementation journey: from call routing to margin setting
23:28 – Q&A: Practical steps for organizations to adopt usage-based models
26:04 – Rethinking AI implementation as a business decision
27:44 – Addressing margin risks with variable LLM costs
30:09 – Reframing hiring and talent strategy for nearshore AI teams
32:23 – Recognizing misaligned workflows beyond tools
35:07 – Revisiting product offerings as AI evolves services
37:48 – Practical steps for shifting to hybrid subscription-plus-usage pricing
40:14 – Cross-department workflow mapping for AI collaboration
42:28 – Managing organizational friction and aligning AI initiatives
43:25 – The Groundhog Day fulcrum: balancing resources and demand
45:16 – Onboarding Latin American remote hires effectively
47:37 – Finding balance between governance and flexibility in enterprise workflows
49:24 – Final thoughts: Measure, adapt, innovate in AI monetization- Ready to action this strategy?
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