What worked for me, on the two occasions I have seen an in house vision build actually succeed, was a very narrow scope and a team that already had computer vision people on payroll. Every other attempt I have watched ended up buying a commercial system eighteen months later, having spent the intervening period learning why the commercial systems cost what they do.
The Build Case Is Real for Some Plants
Open source tooling is genuinely capable now. A competent engineer can assemble a detector, wire it to an industrial camera and get results on a single defect type within a few weeks. If you inspect one part on one line and that part will not change for five years, building can work out cheaper.
The economics also favour building when your defect is unusual enough that no supplier has seen it before. Certain composites, certain food textures and certain speciality coatings fall into that category.
Where the Build Estimate Goes Wrong
Model development is the visible cost and rarely the largest one. The invisible costs are image acquisition hardware, enclosure design, PLC handshaking, operator interface, result logging, model versioning, retraining workflow and the on call rota for when the line stops at midnight.
A useful sanity check is to take your model development estimate and multiply by four. That figure lands close to the true first year cost in the projects I have seen audited afterwards.
The Bus Factor Problem
In house systems tend to live inside one engineer’s head. When that engineer takes a role elsewhere, the plant is left with an undocumented pipeline and a model nobody can retrain. I have seen a working system decommissioned entirely for this reason, replaced by manual inspection while procurement started a fresh supplier search.
Commercial platforms carry their own version of this risk, and at least the documentation and the support contract survive staff changes.
What You Are Really Buying From a Supplier
A mature product bundles years of edge cases you have not encountered yet. Auto exposure recovery when a bulb ages, handling for parts presented upside down, sensible behaviour when the network drops, tooling to compare model versions against a held back dataset. None of these appear in a build estimate and all of them appear in month seven of a build.
Looking at how established vision system companies package hardware, software and integration into one contract clarifies which parts of the problem you would otherwise be solving yourself, and at what internal cost.
The Middle Path Most Plants End Up On
Buy the inspection platform, build the integration around it. Suppliers handle the vision. Your team handles the MES connection, the dashboards and the process rules that are specific to your factory. That split plays to both sets of strengths and keeps the institutional knowledge in house where it belongs.
Comparing on Five Year Cost
Model the whole period rather than the purchase order. Licence fees, retraining charges, support contract, hardware refresh and internal engineering time all belong in the comparison. A shortlist of ai inspection software sorted by upfront price frequently reorders completely once the recurring lines are added, and the cheapest first year option is often the most expensive by year four.
Ask each supplier to quote the same five year scenario in writing, including a variant change every twelve months. The spread in those quotations is educational.
How to Decide in a Week
Count your computer vision engineers. Count how often your products change. Estimate what an hour of unplanned line downtime costs. Those three numbers point at the answer more reliably than any feature comparison table.
If the engineer count is zero, the decision is already made regardless of what the build estimate says. Hiring for that skill set takes six to nine months in most markets, and the person you hire will spend their first quarter learning your process rather than shipping an inspection system.
Build when the problem is narrow and permanent and you already have the people. Buy when it is neither.
