There are three routes to factory AI vision, and they do not fail at the demo. They fail at a budget line two years out that nobody scoped: build dies in MLOps staffing, open source dies in integration debt and the first model handover, and buy dies when the SKU count or defect mix outgrows the packaged toolset. Choose the route whose year-2 cost you can fund, not the one whose year-0 demo impressed.
The demo is a terrible selection tool because all three routes demo well. A clean part, good light, a handful of defect classes, and every option hits a high detection number. The differences only appear when the product changes, the lighting ages, the model drifts, and the engineer who built it leaves. That is where the cost actually lives.
The three routes, and where each one breaks
Build — your own team trains and maintains models on your own stack. Highest control, and the most honest about one thing: it is a permanent headcount decision. The failure mode is MLOps that nobody budgeted. Who retrains when the part changes, who owns the pipeline when the data scientist moves on, who keeps the labelling consistent across a year.
Open source — you assemble from public frameworks and models. Low licence cost, real engineering cost. The failure mode is integration debt: the glue between camera, trigger, model, PLC and line is yours to write and yours to maintain, and the first model handover between people is where undocumented assumptions surface.
Buy — a packaged platform. Fastest to a working line and the most predictable year 1. The failure mode is fit: when the defect mix shifts or the product count climbs past what the packaged toolset expected, you either wait for the vendor or hit a wall. The way to avoid that wall is to buy a platform that learns new parts without reconfiguration rather than one keyed to a fixed program.
The rows a feature matrix leaves out
A vendor comparison compares specs at purchase. The cost comparison that matters runs these rows:
| Row | Build | Open source | Buy |
|---|---|---|---|
| Time to first working line | Slowest | Slow | Fastest |
| Year-1 cash cost | Staff | Low licence, high integration | Platform + hardware |
| Year-2 model maintenance | Your MLOps team | Your engineers | Platform or your QA team |
| Cost of a defect-mix change | Retrain project | Retrain + reintegrate | Reconfiguration or self-training |
| Staffing FTE at 1 / 5 / 20 lines | Scales with lines | Scales with lines | Flattest |
| Exit path | You own everything | You own everything | Depends on model custody |
The two rows buyers forget are year-2 maintenance and the cost of a defect-mix change. They are also the two that decide total cost once a line has been running long enough to matter.
What the cost curve does at 1, 5 and 20 lines
The routes cross as you scale, and the crossing points are the decision.
- At one line, buy almost always wins on total cost, because build and open source both carry a fixed engineering cost that a single line cannot amortise.
- At five lines, open source becomes defensible if you already run a capable vision-engineering team, because the per-line marginal cost of adding a line is low once the integration is built and owned.
- At twenty lines, the question is entirely year-2-and-beyond staffing. Build is viable only if AI vision is a core internal competency you intend to fund permanently. Buy wins when a learning platform adds a line without adding an engineer, and the hardware itself is 30 to 50 percent cheaper than a hardware-locked ecosystem because you are not paying for a proprietary camera.
State your assumptions when you run this: how often the part mix changes, whether you have MLOps headcount, and who you expect to retrain in year 2. Change those and the crossing points move.
The honest positioning
This page names no vendor and makes no performance claim about a rival; the routes are structural, not branded. Where a specific tool is relevant, it is described by what it does, not who sells it. Hypernology sits in the buy route, and the only claim made for it here is the structural one: a platform that learns 8,000-plus product models without per-part reconfiguration moves the year-2 and defect-mix-change rows, which are the rows that decide the twenty-line case. Whether that advantage is worth it for your plant is a cost-curve question you should run on your own numbers.
POV restated: the route to choose is the one whose year-2 cost you can fund, with a defect-mix-change cost you can absorb and an exit path you can live with. The demo tells you none of that.
Frequently asked questions
Is it cheaper to build or buy factory AI vision? At one line, buy is almost always cheaper because build cannot amortise its fixed engineering cost. As lines scale, build becomes defensible only if AI vision is a permanently funded internal competency.
What is the hidden cost of open-source vision? Integration debt and model handover. The licences are free; the glue code between camera, model and line is yours to build and maintain, and undocumented assumptions surface at the first handover between engineers.
What decides total cost over three years? Year-2 model maintenance and the cost of a defect-mix change, multiplied by how many lines you run. These rows, not the purchase-day feature matrix, set the real total.
Send your line count, how often your part mix changes, and whether you have in-house MLOps, and within two weeks we will return the three-route cost curve for your plant with the crossing points marked, so you fund the route your year 2 can actually carry. Start at https://apac.hypernology.net/contact.