Build your own AI or buy off-the-shelf: what to choose for a factory
> Reading time: approx. 7 minutes · Who it is for: owners and boards of mid-sized manufacturers, IT managers, people preparing an AI business case and RFP.
Build your own AI or buy off-the-shelf is rarely settled by price. Building from scratch only makes sense when you have an ML team, time measured in quarters, and a problem unusual enough that nothing ready covers it. For most manufacturers those conditions do not hold, so buying wins on time, risk, and the cost of upkeep, provided you pick the right type of vendor. And not all vendors are alike: a cloud API, a SaaS platform, an on-premise integrator, and self-hosted open-source differ from each other more than building differs from buying. Below we break the decision into five questions, add a matrix of four vendor types, and show the middle option that is most often forgotten: buy the core, build your edge.
| Factor | Cloud API | Vertical SaaS | On-prem integrator | Self-hosted open-source |
|---|---|---|---|---|
| Time to result | days | weeks | weeks to months | months |
| Data leaves the company | yes | usually yes | no | no |
| Cost | per use, grows with volume | subscription | hardware plus deployment | free model plus build cost |
| Control and fit | low | medium | high | highest |
| Who maintains it | the vendor | the vendor | you with an integrator | you alone |
| Entry barrier | lowest | low | higher | high, effectively like building |
Build your own AI (in-house)
- A perfect fit for an unusual workflow that no vendor covers.
- The solution becomes your property and a source of competitive advantage.
- No dependence on someone else's price list or product roadmap.
- Full control over the data and over what changes, and when.
- Months to the first working result.
- Needs an expensive, thin-on-the-market ML team, for upkeep as well as the build.
- All the risk of failure sits with you, with no references and no results gate.
- The full cost of upkeep over years often exceeds a vendor's licence cost.
Buy an off-the-shelf solution from a vendor
- A first result in weeks, because the core already exists.
- Upkeep, updates, and quality sit with the vendor.
- Shared risk, references, and a pilot with a results gate before you commit.
- A lower, predictable entry barrier, good for a first deployment.
- You adapt partly to someone else's way of working.
- Dependence on the vendor and its price list over the longer term.
- An unusual part of the process may go uncovered.
- With the wrong type of vendor, data can leave the company.
Question one: how much time do you have to the first result
This most often settles it on its own. Building your own solution is months before you see anything working: gathering data, choosing a model, iterating, testing. An off-the-shelf solution from a vendor gives a first result in weeks, because the core already exists and you tune it to yourself rather than writing from scratch. If you are under pressure for a fast, countable result, this factor almost always points to buying. Building only holds up when time is not a constraint and the problem is yours and yours alone.
Question two: do you have people to maintain it
Building is one thing, maintaining is another, and the second lasts for years. Your own AI needs a team not only to stand it up but to update the models, watch quality, and react to change. The market for those people is expensive and shallow, and in a manufacturing company other, equally important tasks compete for them. By buying off-the-shelf you shift the weight of upkeep to the vendor. This is one of the most underestimated costs of building: not the launch itself, but the three years of upkeep after it.
Question three: have you counted the full cost of building
This is where most mistakes are made, usually in favour of building, because only a few months of team salaries get counted. The full cost is much more: the time of the board and domain experts pulled off other work, the cost of errors and dead ends that a first project cannot avoid, and upkeep after go-live. A fair comparison puts the full cost of building next to a three-year cost of a vendor licence or subscription. Only then does it become clear that „we will build it ourselves, it is cheaper” is often true only in the first month of the spreadsheet.
Question four: how unusual is your problem
This is the only factor that genuinely defends building. If your workflow is specific enough that no vendor covers it, and at the same time it is a source of your competitive advantage, building your own solution starts to make sense, because you are buying not so much a tool as uniqueness. But be honest here. Most processes in manufacturing, searching documentation, quoting, service handling, quality control, are typical enough for an off-the-shelf solution to cover them. Uniqueness has to be proven, not assumed.
Question five: who carries the risk if it fails
With a build, all the risk is yours. If the project stalls, you have paid for the learning and have no product. With a purchase the risk is shared: the vendor has finished deployments, references, and contractual liability, and you can start from a pilot with a clear results gate before you commit. For a company doing this for the first time, moving part of the risk onto someone who has done it many times is often more important than the difference in price.
Matrix: „buy” is not one thing, there are four vendor types
The biggest oversimplification in this debate is treating „buying” as a single option. There are at least four types of vendor on the market, and they differ from each other more than building differs from buying. The first is providers of large-model cloud APIs: the fastest start, but data goes outside and you pay for usage. The second is SaaS platforms specialised in your industry: a ready workflow, little configuration, but you adapt to their way of working. The third is integrators deploying the solution at your site, on-premise included: full control and data in house, a higher entry barrier. The fourth is open-source stood up on your own: no licence fees, but in effect you return to the cost and risk of building, only on someone else's model. The table below breaks them down against the same factors.
The forgotten option: buy the core, build your edge
The decision is not binary. The most common sensible setup is to buy a mature core from a vendor and build on it the thin layer that is truly yours and unusual. You are not building a model or infrastructure from scratch, only what gives you an edge, and you take the rest ready-made. This combines the fast start and low risk of buying with a touch of the uniqueness of building. For most manufacturers it is a better starting point than either extreme.
How to approach the decision in practice
Start with the question of uniqueness, because only it defends building. If your problem is typical, and it usually is, the discussion comes down to choosing a type of vendor, not to „build vs buy”. Then move to data and upkeep: if the data has to stay in house, cloud API providers drop out and on-premise integrators or self-hosted open-source remain. Finally, count the full cost of three years, not the first month. The same order-of-decision logic returns in our notes on assessing AI readiness and on the ten questions before the RFP: the hard factor first, then cost, convenience last.
What this comparison does not cover
We do not go into choosing a specific named vendor or into prices, because they depend on your volume and starting point. We also do not settle the on-premise versus cloud dispute, because that is a separate decision we broke down in the comparison of on-prem or cloud for a factory. The focus here was the earlier call: build it yourself or buy, and which type of vendor fits your situation. How NIS2 looks at an AI provider from the supply chain angle is broken down by aionprem in its analysis of public cloud LLM and NIS2.
Related
Verdict
If your problem is typical, and it usually is, buying wins on time, risk, and the cost of upkeep, and the whole decision comes down to picking the right type of vendor. Building only holds up with an unusual, proprietary problem, an ML team, and time measured in quarters. When the factors conflict, the best starting point is to buy the core and build your edge.