How to test your AI usage-based pricing to maximize revenue

I worked on building usage-based billing for an AI service, starting with a credits system that models value as credits through a single balance and a labeled history. I focused on getting Stripe billing right for AI usage patterns, deliberately navigating the provider quirks that typically break usage-based billing. I built reliable reconciliation to match provider charges to our database as the source of truth, ensuring the internal ledger remains authoritative. I designed cancellation and trial flows that include retention saves, trial mechanics, and abuse limits to reduce churn and guard against exploitation. I also made the pricing model decision, evaluating usage versus seat versus hybrid approaches and determining whether to build the billing system now or wait.

I approach this work by treating the database as the source of truth and treating every external charge as a claim that must be reconciled, not accepted blindly. I focus on the edge cases where provider behavior diverges from intuitive billing logic, and I design credits, trials, and cancellations as interconnected systems rather than isolated features. I weigh the trade-offs between giving users flexibility and enforcing the hard limits that prevent revenue leakage. Ask me about designing a credits system with a single balance and labeled history, avoiding Stripe quirks that break AI usage billing, building reliable reconciliation that matches provider charges to your DB, structuring cancellation and trial flows with retention saves and abuse limits, or deciding between usage, seat, and hybrid pricing and whether to build now.