AI Build vs. Buy ROI Calculator

Building an AI capability in-house feels like control. It is also a hiring cliff, a long wait for value, and a real chance the project never ships. Set the build scope and the buy/partner side scales with it a bigger build implies a bigger capability to buy, so the two move together instead of comparing apples to oranges.

The build side

Senior engineers to hire

Fully loaded cost per engineer ($ / year)

Hire + ramp before value (months)

Build duration (months of active build)

Ongoing maintenance (% of build cost / year)

Failure haircut (% chance the build never pays off)

The buy / partner side

Buy / partner priced at (% of equivalent build delivery)

Buy / partner cost ($ / year — auto-scales, editable)

Time horizon (years)

What it costs

In-house, total over horizon

Buy / partner, total over horizon

Net difference

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Break-even point

Adjust the inputs above and the numbers update live.

The cheap option on paper is not always the right one. Building can win on cost and still lose you a year of market position, or sink under a team you cannot keep busy. Buying can win on speed and lock you to someone else's roadmap. The answer depends on which risk you can afford.

Book a 1:1 call with one of our AI engineers to pressure-test this decision with your real constraints. No pitch.

Estimates only. The buy/partner cost defaults to a share of the equivalent build's delivery cost (team size × time-to-first-value), so a larger build scope raises the buy figure too — override it with a real vendor quote when you have one. Salaries, ramp times, and failure rates vary by team and problem. Logiciel Solutions · logiciel.io