Blog
Practical guides, architecture deep-dives, and decision frameworks for AI founders and engineers monetizing AI products.
Early-stage founders often see their small customer base as something to overcome. But when it comes to pricing decisions, having fewer customers creates specific advantages that become harder to maintain as you grow.
The best Stripe alternatives for usage-based billing in 2026 split between real-time and invoice-based architectures. Compare solutions for AI products.
Five PSP integration patterns shape how AI billing platforms fit with existing payment setups. Compare trade-offs and pick the pattern that fits your stack.
Metered billing charges customers for measured usage, not flat fees. See how metering works, five pricing models, and which platforms fit your product.
Connect your PSP to an AI billing system via a push model: your PSP processes top-ups, your backend creates a balance adjustment. See the 5 steps and pitfalls.
Lago offers open-source usage billing with paid premium features. Credyt provides real-time wallet billing with a portal included. When does it make sense to build vs buy?
Stigg controls access and monetizes across your billing system. Credyt adds margin observability and ships with AI tools like Claude Code and Lovable. Compare fit.
Consumption based pricing requires more than code. Covers billing architecture trade-offs, the observability step most teams skip, and how to launch or migrate.
Usage-based billing charges customers by actual consumption, not flat fees. See how it works, compare five pricing models, and why AI products need a fundamentally different approach.
Four places where fraud and revenue leakage hit AI products using Stripe metered billing. Practical fixes from Radar to prepaid wallets.
Step-by-step breakdown of Stripe's metered billing: object chain, meter events, webhooks, pricing changes, and customer portal. With working code examples.
AI products incur real costs in real time, but most billing systems were built to record what happened. They can't control what happens next. As of Q1 2026, the gap between infrastructure costs and billing capabilities is one of the most common unit economics problems that AI companies discover too late. This article explains why billing and economic control are separate problems, and what it looks like when both are solved.
Stripe and Tempo launched the Machine Payments Protocol yesterday. The scope is deliberately narrow. Understanding precisely what it covers makes the adjacent problem more visible. This piece covers what MPP doesn't solve: wallet management, real-time balance tracking, and margin observability, and how the billing layer above the protocol needs to work.
Most companies don't replace their billing infrastructure. They evolve toward it. Shadow Mode, Hybrid Mode, and Full Wallet Control are three stages that let AI companies gain real-time economic control without the risk of a billing migration.
Clay overhauled its pricing model this week, cutting data costs by 50–90% and introducing a two-meter system that separates enrichment costs from platform work. The changes materially lower the barrier to entry and signal a deliberate bet on volume over margin. Here's the full breakdown.
AI coding tools created a new class of builders. They ship functional apps without engineering backgrounds. Then they hit a wall the moment they try to charge for what they've built.
Free to start. Live in hours. No engineering team required.
