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AI economics

Agent Cost Per Validated Customer

What does each activated, retained user cost you in AI and infra spend? Run the per-validated-customer math.

Your numbers

Start from an example

Activation rate
30-day retention
tok

Input plus output tokens. Priced as a 50/50 split.

$
$
$
$

Result

Cost per validated customer
$5.46
Total monthly cost
$4,300
Validated customers
788
Status: in range.Gross margin on paying customers
72.7%

Monthly cost $4,300 = AI tokens $1,800 + infra $2,500. Revenue from 788 paying customers at the entered price: $15,760 a month.

Activation funnel

Every MAU costs tokens and infra; only validated customers carry the cost.

Monthly active users
5,000
Activated
1,750
Validated (activated and retained)
788

General business information, not legal, tax or financial advice. The result comes only from your inputs and the published method. How we check our numbers.

How to use it

  1. Enter your monthly active users, your activation rate, your 30-day retention rate, the tokens each user consumes per month, your model's input and output prices per million tokens, your infra cost per user, and optionally your price per user. The tool splits token usage evenly between input and output, so enter total monthly tokens; with current pricing such as Claude Sonnet 5.5 at $2 input and $10 output per million, or Claude Haiku 5.5 at $0.10 and $0.50, the model you choose dominates the cost line.
  2. Read the cost per validated customer, total monthly cost (split into AI tokens and infra under the tiles), and the count of validated customers (active users times activation times retention). A validated customer is one who activated and stuck around for 30 days, so this metric divides your full variable cost by the users who actually matter, not the raw signups that token-burn alone would flatter.
  3. Use cost per validated customer rather than cost per signup, because the gap between them is where AI products quietly lose money. If only a third of users activate and half of those retain, your real acquisition cost can be six times the naive per-user figure, and a price that looked profitable against signups can be underwater against the customers who stay.
  4. Enter your price per customer and read the gross margin on paying customers: revenue from validated customers only, against AI and infra spend for every active user. When the margin is thin or negative, the highest-leverage fixes are usually a cheaper model tier for routine calls, prompt and token reduction, or improving activation and retention so the same AI spend is amortised across more validated customers.
  5. Re-run whenever model pricing changes, when your activation or retention shifts, or when you consider a price change. Pair this with the model price drop stress test to see how falling token costs reshape these economics, and the AI product margin tool for the full per-user cost picture.
  • Run the numbers Runway With AI Cost Shock

    Stress-test runway against an LLM vendor price hike with break-even revenue trajectory.

  • Run the numbers Free Trial to Paid Target

    Reverse-solve trial→paid conversion rate from revenue target, signups, and ARPU.

  • Run the numbers AI vs Human Support Cost

    Compare AI-first and human-only support cost with token spend and escalation overhead.

  • Run the numbers AI Feature Attribution

    ARR attributable to AI features, net of infra cost, with cohort gross margin and retention lift.

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Understand the costs, margins, and pricing of building AI-powered products.

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Use it from code

The same calculation as a JavaScript module you can import. It runs where you import it, with no request, key or rate limit.

import { compute } from "https://aibizhub.io/engines/agent-cost-per-validated-customer.js";

Input and output contract, TypeScript types and the guide for agents.