How to Control Overhead Cost for AI Products
Every LLM call in your AI product — a chat message, a doc summary, an MCP tool call — has a non-deterministic cost attached to it. Let a customer upload 1,000 documents instead of 3, and you're getting an invoice from Big LLM you didn't budget for.
The short version of how you get that under control: measure margin per account, per feature, and per vendor — not just cost. Set enforcement limits that guarantee your worst case. Then price so revenue moves with usage instead of trailing behind it.
Cost-to-deliver is the metric most teams reach for first, and it's a good start. But it's incomplete, because it's missing revenue. Controlling cost alone only ever caps your downside. The number that actually gives you control is margin-to-deliver.
Margin-to-deliver is the share of what you charge for a unit of delivered value that you keep after the vendor cost of producing it —
(charged − overhead) / charged— measured per account, per feature, or per vendor.
Optimize margin-to-deliver and you've got two levers to pull: enforcement and pricing. Here's how we think about both, and the order we'd tackle them in.