What AI teams build with AIPricingLab
Concrete patterns with code: how to meter LLM tokens per user, gate image generation, bill multi-step agents, ship freemium AI products, and price by tokens.
Each page is a small implementation brief - the problem, the solution, and a runnable code snippet. If your use case is not listed, the patterns generally compose: token-based pricing can be layered onto agent billing, image quotas can be combined with freemium tiers, and so on.
LLM usage metering: track tokens per end-user, across providers
Meter LLM token usage per end-user across OpenAI, Anthropic, Gemini, Mistral, and any other provider. Composite events for prompt + completion tokens, real-time per-user limits, atomic enforcement. The drop-in pattern for AI apps.
Use caseImage generation quotas: per-user limits for DALL·E, Flux, Stable Diffusion
Enforce per-user quotas on image generation across DALL·E, Flux, Stable Diffusion, Midjourney API, and Replicate. Atomic reservation pattern stops parallel renders from overshooting. Free tier, premium tier, hard caps - drop in.
Use caseAI agent billing: meter multi-step agents and tool calls
Metering AI agents is harder than metering single LLM calls. One "agent run" can fan out into 20 tool calls and 50 LLM calls. AIPricingLab handles agent-level and step-level metering with composite events and atomic reservations.
Use caseFreemium AI SaaS: ship a free → paid funnel without a backend
Build a freemium AI product where the free plan has hard quotas, the paid plan unlocks more, and "you have used 80% of your free renders" nudges drive upgrades. Drop-in implementation, ten minutes from zero to live.
Use caseToken-based pricing: charge users for actual AI consumption
Charge AI app users by tokens, requests, or compute seconds. Pre-paid credits, post-paid invoicing, hybrid models - implementation patterns and trade-offs from someone who has shipped all three.
Want a step-by-step walkthrough?
See the full guides library - in-depth tutorials with end-to-end implementations.