Quick answers to AI metering questions
Short, specific answers to questions developers ask while building AI products. Each question links to the relevant guide if you want a deeper walkthrough.
How do I track LLM usage per user?
Call apl.track(userId, "llm.tokens", tokenCount) after each LLM call. AIPricingLab counts it against the user's plan limits in real time and exposes a per-user …
Q&AHow do I rate-limit OpenAI calls per user?
Use apl.reserve(userId, "openai.chat", 1) before the OpenAI call. If allowed=false, return 429. If allowed=true, call OpenAI then commit on success or release o…
Q&AHow do I implement freemium for an AI product?
Define two plans (free, pro) with different limit groups. Assign free on signup. Gate AI calls with reserve / commit / release. On limit_reached, show an upgrad…
Q&AHow do I charge AI app users by token usage?
Define a limit group with unit "tokens" or "cents". Reserve an upper bound before the AI call, commit on success, refund the unused portion. For pre-paid: bump …
Q&AWhat is metered billing for AI apps?
Metered billing for AI charges users based on actual consumption - tokens, image renders, agent runs - instead of a flat subscription. Two layers: a metering ba…
Q&AHow do I track image generation usage per user?
Call apl.reserve(userId, "image.render", 1, { model: "flux-pro" }) before calling the image provider; commit on success, release on error. AIPricingLab counts e…
Q&AWhat is the reserve / commit / release pattern?
reserve atomically holds quota with a 60-second TTL; commit confirms the reservation after the AI call succeeds; release rolls it back on failure. Reservations …
Q&AHow do I add quotas to my AI app?
Define limit groups in the AIPricingLab dashboard with the unit and quota you want, attach them to a plan, assign the plan to each user. Gate AI calls with apl.…