Is this accurate enough to decide?

It is accurate for the assumptions you enter. Use it for planning, then validate the most sensitive inputs with primary sources when stakes are high.

What if I don’t know an input?

Model a range (low/base/high). If the decision flips across the range, that input is your next research priority.

Run a base case, a downside case, and a managed response case. If the plan survives the downside, the decision is more robust.

SaaS Desk

Three scenario templates that show how LLM API cost behaves when assumptions change.

SaaSUpdated April 12, 20261 min read

Examples are the fastest way to catch hidden assumptions. The goal is not the exact number—it’s understanding how LLM API cost moves.

Start with a conservative base case, then test a downside shock and a managed response.

Use realistic values for input tokens, output tokens, and requests per user. Record outputs like monthly cost and cost per user.

If the base case fails your decision threshold, you do not have a safe plan yet.

Worsen one variable meaningfully while keeping others constant.

If one shock breaks the plan, build a buffer or mitigation strategy.

Assume the downside happens—and you respond using levers you can execute.

The managed case is your plan B. Write it down before the shock happens in real life.

Interactive Tool

Model the numbers behind AI API Cost Examples: 3 Scenarios to Model (Base, Downside, Managed)

Stop reading theory. Jump into the interactive calculator to model your exact scenario instantly.