aicost.ai
VC/PE Diligence · AICost.ai cost decision engine

📈 What is the cost lever worth at exit?

Convert achievable AI and cloud savings into basis points of EBITDA expansion, value it at your exit multiple, and roll it up across the portfolio at a realistic adoption rate.

Inputs

Enter what the company claims. Everything recomputes live.

Portfolio company revenue.
$
As reported today.
%
The addressable inference/AI cost line.
$
The addressable infrastructure line.
$
Routing, caching, batching, model right-sizing. Model the workloads before underwriting this.
%
Right-sizing, commitments, zombie capacity, placement.
%
Your own assumption, not a market quote.
x
One-off, used for payback.
$
Set above 1 for a fund-level roll-up.
Roll-ups fail on adoption, not on math. Haircut it honestly.
%
Verdict
MATERIAL LIFT

375 bps sits inside the 200-400 bps range PE operating teams target from AI and cloud cost work.

⇩ Download PDF memo View JSON All diligence tools
Margin expansion
375 bps
EBITDA margin
15% → 18.8%
Annual savings
$1,500,000
from AI
$900,000
from cloud
$600,000
Enterprise-value lift
$18,000,000
EV lift vs today
25%
Payback
2 months
Year-one net
$1,250,000

Deal memo

On $40,000,000 of revenue at a 15% EBITDA margin, the addressable AI and cloud cost base is $6,000,000. Applying 30% AI and 20% cloud savings releases $1,500,000 a year, lifting EBITDA from $6,000,000 to $7,500,000, an expansion of 375 bps to a 18.8% margin (material lift). At a 12x exit multiple that is worth about $18,000,000 of enterprise value, roughly 25% of today's implied EV. Implementation pays back in about 2 months, with $1,250,000 net in year one. The savings rates are the assumption to test: model the actual workloads before underwriting them.

Questions for the founder

  1. What share of the AI spend runs on the most expensive model tier, and could a routing policy move it without hurting quality?
  2. What is the current cache-hit rate, and what would a serious caching layer do to cost per request?
  3. Is any cloud capacity committed or reserved, and what is the utilization against it?
  4. Who owns cost in the org today, and is there a budget or guardrail enforced in the gateway?
  5. The model assumes 30% AI and 20% cloud savings. What evidence supports those rates for this specific workload mix?
  6. Does the plan survive if the company's AI usage doubles during the hold period?

Assumptions

  • Cost savings are treated as flowing to EBITDA close to 1:1, which holds for opex-classified AI and cloud spend.
  • Savings rates of 30% (AI) and 20% (cloud) are analyst inputs, not measured results. *
  • Exit multiple of 12x is the fund's assumption. *
  • Excludes revenue-side effects, headcount changes, and any capitalized spend.

Values marked * are analyst estimates rather than vendor-verified data.