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Unlike SaaS with near-zero marginal cost per user, an AI product carries real COGS on every request, so this template treats GPU compute as the primary cost of goods. It starts from inference volume, users times API calls per user per month, multiplies by revenue per call for gross revenue, then subtracts GPU hours times cost per GPU hour to get gross profit. Cost per inference is tracked as the fundamental unit economic, the AI equivalent of COGS per unit in manufacturing, because it's where margin is won or lost.
AI businesses should target 50–70% gross margin after compute. LLM API products typically run 40–60% under high inference costs, image generation 50–65%, and code assistants or analytics AI can reach 60–75% with efficient batching and caching. Below 40% gross margin is a warning sign that pricing or model efficiency needs work before scaling. The model pre-loads these ranges so a usage-based pricing tier isn't set below its own cost to serve.
The defining risk of an AI model is that cost scales non-linearly with usage, grow traffic and compute cost grows with it, unlike SaaS where the next user is nearly free. That makes gross margin variable and dependent on model efficiency, hardware cost, and inference optimization. This template models batching and caching effects against traffic so you can find the point where revenue per call clears cost per call with enough margin for the AI product to become self-sustaining rather than compute-subsidized.
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Gross Margin is the percentage of revenue remaining after subtracting the direct costs of ...
Net Revenue Retention measures the percentage of recurring revenue retained from existing ...
ARPU measures the average monthly revenue generated per active user or subscriber. It refl...
Burn rate is the net amount of cash a company consumes each month. It measures how quickly...
Runway is the number of months a company can continue operating at its current burn rate b...
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