The competent way to buy frontier-model access for a team is flat seats for the people doing
interactive work, plus a shared metered API account for the automation. This calculator sizes both,
then shows what you would have overpaid by metering every engineer instead — the seat saving.
Start from a preset:
Field data: 50–70% sustained adoption is typical
A premium coding-assistant subscription, per active engineer
Metered tokens for pipelines, CI, and batch jobs
What metering an engineer's interactive usage would cost — the false-economy figure
Your estimate
Active seats—
Seat spend / month—
Shared API / month—
Total / month—
Total / year—
Cost per engineer / month—
Seat saving vs metering everyone—
Adjust the inputs to size your team's AI access.
How to read the result
The total per month is the honest cost of the competent setup: seats for people,
shared API for pipelines. The seat saving is the gap between that and the naive
alternative of metering every engineer's interactive usage — the money you lose by treating people as
pipelines. If the saving is large, your instinct to "just use the API" would have been expensive. For
the reasoning behind the seat-versus-token decision, see
what frontier-model access should cost a team;
for the per-tool free-tier and pricing data, We The Flywheel's
cheapest-access cluster.
LLM Cost Calculator: Frequently Asked Questions
What does this calculator estimate?
It models the monthly and annual cost of giving your engineering team frontier-model access using the competent setup (flat seats for the people doing interactive work, plus a shared metered API account for automation), and compares it against the naive alternative of metering every engineer's interactive usage. The gap between the two is the "seat saving": the money you lose by modelling people as pipelines.
Why separate seats from the shared API spend?
Because they bill on different logic. A seat is a flat fee a human cannot out-consume, so it is the cheapest way to cover interactive, human-in-the-loop work. The metered API charges per token, so it is the cheapest way to cover automated, headless workloads that run without a person pacing them. The right architecture uses both; this calculator keeps them as separate inputs so you can size each honestly.
What is the "naive metered" comparison?
It is what you would pay if you put every active engineer on metered API billing for their interactive work instead of a flat seat. Because a person can run up real token bills over a full day of coding, this number is almost always higher than the seat — often by a large multiple. It is the false economy the calculator is built to expose.
Are these default numbers accurate for my team?
They are reasonable 2026 starting points — a premium coding-assistant seat in the $25–30 range, realistic adoption rates, and shared-API figures that scale with team size. They are not your numbers. Override every field with your actual seat price, adoption, and automation spend; the defaults exist to give you a sensible starting shape, not a quote.
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Thomas PrommerTechnology Executive — CTO/CIO/CTAIO
These salary reports are built on firsthand hiring experience across 20+ years of engineering leadership (adidas, $9B platform, 500+ engineers) and a proprietary network of 200+ executive recruiters and headhunters who share placement data with us directly. As a top-1% expert on institutional investor networks, I've conducted 200+ technical due diligence consultations for PE/VC firms including Blackstone, Bain Capital, and Berenberg — work that requires current, accurate compensation benchmarks across every seniority level. Our team cross-references recruiter data with BLS statistics, job board salary disclosures, and executive compensation surveys to produce ranges you can actually negotiate with.
