Every company adopting AI hits this fork: build the capability in-house, or bring in outside help? Both camps have loud advocates, usually selling the thing they advocate. We sell one side of this — embedded AI developers and project builds — so here is the math laid out honestly enough that you can catch us being self-serving.
The real cost of in-house
A senior AI engineer in the US or Western Europe runs a fully-loaded cost (salary, benefits, equity, payroll, tooling) well into six figures — and that is after a search that typically takes months and a recruiting fee on top. A functioning team is rarely one person: you need someone who can do the data engineering, someone product-minded, and someone senior enough to make architecture calls. Realistically, a minimal in-house AI team is a multi-hundred-thousand-dollar annual commitment that starts producing roughly two quarters after you decide to build it.
None of that makes it wrong. It makes it an investment with a threshold: in-house wins when AI is core to your product and the work is continuous. If your roadmap has years of AI work on it, owning the capability compounds — institutional knowledge, faster iteration, no vendor dependency.
The real cost of an agency
An agency or studio charges more per hour than an employee costs per hour. In exchange: output starts in weeks, not quarters; you pay only while the need exists; and — with a good one — you get people who have shipped the failure modes already, on someone else’s budget. (What that pricing looks like in practice.)
The failure modes are real too: black-box vendors who leave nothing behind, juniors billed as seniors, and the dependency trap where nobody in your company understands the system you now run your business on. (Our guide to choosing a vendor is mostly a field guide to dodging these.)
The decision table
| Your situation | Lean |
|---|---|
| AI is the product, funded for years | In-house — own your core |
| One significant system to build, then operate | Agency project with a proper handover |
| Continuous AI roadmap, but can’t hire fast enough | Embedded/dedicated developers while you recruit |
| Not sure yet what AI should do for you | Strategy sprint first — cheapest possible step |
| Product team exists, AI depth missing | Hybrid — see below |
The hybrid most companies actually land on
The pattern we see work repeatedly: outside help builds the first system and the standards around it — evaluation, monitoring, runbooks — while the company hires deliberately behind it. The external team’s exit is planned from day one: documentation, working sessions, and eventually helping write the job spec for their own replacement. The company ends up with a working system and an in-house owner who inherited working standards instead of a blank page.
The anti-pattern is the same story without the exit plan — three years later the vendor is load-bearing, unbudgeted and irreplaceable. If you take one sentence from this post: whatever you choose, write the ownership handover into the first contract.
Questions that settle it quickly
Is AI your differentiator or your tooling? (Differentiator → build toward in-house.) Is the work a project or a stream? (Project → buy it; stream → staff it.) Can you afford six months of nothing while you hire? (No → start external, hire in parallel.) Does anyone senior in-house want to own AI? (No → fix that before either path, because unowned systems rot regardless of who built them — it is failure mode #5.)
If you want the version of this conversation about your actual roadmap, get in touch — including the case where the honest answer is “hire, don’t outsource.” It costs us a deal occasionally. It is also why people believe our proposals.