Archived 2025 Research ProjectKotaML is a case study in AI inference economics.

Pricing data reflects public list pricing last updated in December 2025.

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KotaML
Data & Tools/Margin Analysis

Margin Analysis

(Pricing Data As Of December 2025)

A

Cost

Select provider/model and confirm token pricing.

B

Volume

Converts per-request assumptions into monthly totals.

C

Pricing

Defines revenue model and target gross margin.

D

Outputs

Gross margin computed from inferred monthly inference cost and the selected revenue model.

Usage Increase
MAU Held Constant
Output Increase
Stress Test
Monthly Inference Cost (Selected)
$0.00
Today: $0.00
Revenue (Selected)
$20,000
Price × MAU
GM (Selected)
100.0%
Stress Case Applied
Usage Scenarios
Scenario
Requests/User
MAU
Revenue
Cost
GM
Today
50
1,000
$20,000
$0.00
100.0%
2× Usage
100
1,000
$20,000
$0.00
100.0%
5× Usage
250
1,000
$20,000
$0.00
100.0%
10× Usage
500
1,000
$20,000
$0.00
100.0%
UNSAFE: Revenue Is Effectively Zero At Scale.
Set A Non-Zero Price To Compute GM And A Target-Closing Recommendation.
E

Break-even analysis

Derived threshold where unit economics break, based on current pricing and costs.

Break-Even: At Current Pricing. Break-Even Usage Per User/Month (GM = 0%) At Current Per-Seat Pricing.
Margin Sensitivity
Gross Margin vs Usage (1× to 10×)
100.0%75.0%50.0%25.0%0.0%-25.0%1×2×5×10×0.0%Target 70.0%Gross MarginRequests Per User Per Month (Usage Multiple)
Baseline GM
Stress GM (+50% Output)

1) Pricing data as of December 2025. Cached input tokens were excluded due to inconsistent reporting.

2) This dataset reflects public list prices only, enterprise and volume discounts are not included.