Choosing between building a custom machine learning system and buying an off-the-shelf SaaS product is rarely obvious from list price alone. This simulation renders a 3D bar chart of cumulative total cost of ownership (TCO) for both paths, year by year, plus a 3D line chart tracing each path's running total so you can see exactly where — if ever — the two lines cross.
Many "buy" decisions quietly become "build" over 3–5 years once integration, customization and per-seat scaling costs compound — which is exactly why TCO modelling, not sticker price, should drive the decision.
A 3D bar-and-line chart of cumulative total cost of ownership, comparing a custom-built machine learning system against a SaaS solution year by year, with a live marker showing exactly when — or if — one option overtakes the other.
Build cost is dominated by an upfront engineering investment plus ongoing maintenance; Buy cost scales linearly with seats and time, plus a one-time integration cost that grows under regulatory constraints. Watching both curves reveals the crossover point.
Adjust time horizon, engineering team size, SaaS price per seat and seat count, and toggle regulated/sensitive data. Bars and the trend line animate live; the gold marker flags the year Build becomes cheaper than Buy.
Per-seat SaaS pricing that looks cheap at 10 users can cost more than an in-house build once an organisation scales past a few hundred licences — the core insight behind most build-vs-buy TCO models.