Your engineers can do this. That is not the question. The question is whether the first one should be theirs, given that first builds carry the learning cost and your team is already committed to a roadmap.
| In house | Outside team | |
|---|---|---|
| Direct cost | Looks like zero. It is the roadmap work that does not ship. | Visible and fixed |
| Time to first production use | Three to six months typical, longer if it competes with feature work | Six to twelve weeks |
| Learning curve | Paid once, kept forever | Paid by the vendor, transferred only if you require it |
| Biggest risk | It becomes the thing everyone gets to next quarter | You end up dependent on someone who leaves |
| Who runs it after launch | Already clear | The thing to negotiate up front, not later |
| Best when | You have slack capacity, a clear owner, and time to be wrong once | There is a deadline, or the first build must be right |
In house reads as free because no invoice arrives. The real price is the roadmap item that slips, and the slip is usually larger than expected, because a first AI build has an unfamiliar shape: evaluation, prompt and retrieval iteration, and a review loop that is not like normal QA. Teams routinely estimate this like a normal feature and land at two to three times the estimate.
That is not an argument against in house. It is an argument for estimating it honestly, then deciding.
Outside teams can leave you with a system nobody internally understands. This is a real and common outcome, and it is avoidable, but only if you make it a term of the engagement rather than a hope: your engineer in the code from week one, documentation as a deliverable, and a named internal owner before launch, not after.
If a vendor resists that, the resistance is the information.
For a first build with a deadline, the shape that works most often is neither pure option: an outside senior engineer builds alongside one of yours, and the handover is a contract term with a date. You buy the speed and the pattern, your team keeps the system. The second build is then genuinely in house, and it is much cheaper than the first.
If there is no deadline and you have real capacity, build it yourself. The learning is worth more than the months.
Related reading: why 95% of AI pilots never reach production · what an AI diagnostic includes · a forward deployed engineer vs hiring