Pricing & monetization

AI features broke the old SaaS pricing playbook. Every video generated has a real cost underneath (voice synthesis, rendering, model calls), and those costs vary by model. Price too generously and margins evaporate quietly. Price too cautiously and nobody upgrades.

My grounding here predates the AI era. Three years at Baremetrics (analytics for SaaS companies) steeped me in MRR, trial conversion, and churn, and I read the entire Stripe API documentation so I could speak the same language as our customers. That instinct for going a few layers below the UI turns out to be most of what pricing work actually is.

• The credit system for AI-generated video: credits mapped directly to underlying model costs, so margins stayed fixed no matter which model a customer picked. New AI features launched with their economics understood in advance.
• Paywall placement and experimentation. Strategically placed paywalls lifted free-to-paid conversion by about 40%.
• The free-tier video limit: cut videos created by free users by about 30% with no measurable dent in conversions. A pure cost-control lever that didn't touch revenue.
• Plan and pricing design for the voiceover era: minute limits, tier boundaries, and the upgrade paths between them.
• The experimentation infrastructure itself, set up in my PM years, so every one of these calls was measured instead of argued.

Monetization stopped being a quarterly scramble and became a system. Pricing conversations moved from opinions to margin math, paywall changes shipped as experiments with readable results, and the credit model scaled through each new wave of AI features without a re-architecture.

It also changed how I think about the craft. Pricing is a product surface, not a spreadsheet. The paywall is often the single most-viewed screen in the product, and it deserves the same design attention as the editor.