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AI ECONOMICS · UNIT COST

Agent Cost Per Validated Customer

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

Try a preset

Activation rate
30-day retention
tok
$
$
$
$

Result

AI COST / VALIDATED CUSTOMER
$6.60
TOTAL MONTHLY COST
$5,200.00
VALIDATED CUSTOMERS
788
Status: in range.GROSS MARGIN AT PRICE
94.8%

Cost vs Validated Customer

Total monthly AI+infra spend divided across activated, retained users.

Total monthly cost
$5,200.00
Cost / validated customer
$6.60
Methodology → Formula, assumptions, sources, and known limits.

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 4.6 at $3 input and $15 output per million, or Haiku 4.5 at $1 and $5, the model you choose dominates the cost line.
  2. Read total monthly AI cost, total monthly infra cost, total monthly cost, the count of validated customers (active users times activation times retention), and the cost per validated customer. 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. If you entered a price, read the gross margin and gross margin percentage at that price to see whether your unit economics hold. 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 (token prices have fallen sharply over the past year), 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.
Questions people usually ask
How can I get decision-grade output quality?

Use validated baseline numbers, run downside and upside scenarios, and align assumptions with your real cadence and constraints.

Is this legal, tax, or accounting advice?

No. Outputs are business planning estimates and should be reviewed with qualified professionals when required.

Is this free and private?

Yes. Tools run client-side in your browser with no signup.

Related Resources

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