Estimation question

Estimate the LLM inference cost per active Windsurf developer.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Estimation of per-user AI infrastructure cost, requiring a reasonable model of a developer's daily interaction volume.

State the goal: estimate the monthly LLM inference cost to serve one active Windsurf developer.
Assume daily usage: around 20 working days a month, with roughly 40 meaningful AI interactions per day across autocomplete and Cascade-style agentic requests.
Assume an average interaction size, say 3,000 input tokens (code context) and 400 output tokens, since agentic flows can pull in more surrounding code than simple autocomplete.
Compute monthly totals: 40 times 20 equals 800 interactions; total input tokens around 2.4 million, output tokens around 0.32 million.
Apply an illustrative blended price, say 3 dollars per million input tokens and 15 dollars per million output tokens, clearly marked as an assumption.
Multiply: (2.4M/1M times $3) plus (0.32M/1M times $15) equals about $7.20 plus $4.80, roughly $12 a month per active developer, before accounting for the more expensive but less frequent full agentic runs.

Separates lightweight interactions (autocomplete) from heavier agentic runs conceptually, even in a simplified blended estimate.
States all assumptions (interaction count, token sizes, pricing) clearly and separately.
Sanity-checks the result against a plausible subscription price to reason about margin.
Notes that a small share of power users running many Cascade-style multi-step tasks would push their individual cost well above this average.
Shows the arithmetic step by step rather than asserting a final number.

Treating all interactions as equally expensive, ignoring that agentic multi-step flows cost more than simple autocomplete.
Not stating the usage assumptions clearly.

How would this differ for a developer who heavily uses Cascade versus one who mostly uses autocomplete?
How would you reduce this cost without hurting suggestion quality?
How sensitive is this number to model pricing changes?