Porównanie

Stennir vs hiring an in-house AI engineer — which makes sense when?

Hiring a senior AI engineer in-house is the most honest competitor to an AI consultancy engagement, by ROI math. The right answer depends on how many AI projects you'll sustain per year, your tolerance for recruiting cycles, and whether your existing team can absorb the work between projects.

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Ostatnia aktualizacja: 2026-10-07

Starcie

Różnice, nazwane wprost

  • Time to first shipped tool

    Stennir
    The first workflow is planned and written down once the scope is signed, with an optional audit before it
    hiring in-house (senior ai engineer)
    Months (write the jd, recruit, interview, notice period, onboard, then start)

  • Breadth of skills

    Stennir
    Senior team with specialist depth across data, ml, infra, UX, ops
    hiring in-house (senior ai engineer)
    One person's depth — strong somewhere, lighter elsewhere

  • On-cost

    Stennir
    Scope and terms agreed in writing on the call; no benefits, recruiting or manager time on your side
    hiring in-house (senior ai engineer)
    Salary + benefits + equity + manager time + tools + insurance + recruiter fees

  • Between projects

    Stennir
    The build ends cleanly. Managed AI operations is ongoing and optional, so you keep the run only while you want it
    hiring in-house (senior ai engineer)
    The hire is idle, working on other things, or leaving — overhead continues

  • Governance

    Stennir
    Shipped with the engagement (policy, audit, rollback plan included)
    hiring in-house (senior ai engineer)
    Depends on the hire's experience — often built ad-hoc

  • Scaling the work

    Stennir
    The next workflows are added as the first one proves itself
    hiring in-house (senior ai engineer)
    Hire another, repeat the recruiting cycle for each

  • Long-term knowledge accrual

    Stennir
    Documented in a runbook and handed over; the AI operator program trains the people who run the workflow, so the knowledge sits in your team too
    hiring in-house (senior ai engineer)
    In-house ownership — the hire learns your business deeply and stays (if they stay)

When hiring in-house wins

You'll sustain three or more AI projects per year. You have an existing engineering organization that can absorb the hire between projects (so they're not idle). You need someone who is also a leader or manager — recruiting future engineers, owning the org's AI strategy, building internal credibility. The work has a long enough horizon that the onboarding period pays back.

When Stennir wins

You have one or two AI projects this year, not a sustained pipeline. You don't have an engineering organization to absorb a senior hire (so they'd sit idle or own work outside their craft). You need the work to ship without a recruiting cycle: one workflow live with real users. You want governance shipped with the implementation, not built after the fact, and the people who use the workflow trained on it.

The hybrid that often beats both

Ship the first one (or two) with Stennir — fast, with governance, with a runbook your team can read. Then hire in-house once you have a real understanding of the scope and shape of the work the org will do at scale. The hire's first quarter is then operating a working system, not building one from a blank page.

The hybrid also de-risks the hire: you've already learned what good looks like at your org, so the jd is sharper and the interview signal is better.

The middle option: a forward deployed engineer

If you want an engineer working inside your team without opening a recruiting process, Stennir can place a forward deployed engineer with you. A named senior engineer works in your tools and on your data, builds the first jobs with your people, and leaves the code in your repository with a runbook. AI training for the people who will run it is part of the plan, so the skill stays with your team. It is a good way to learn the shape of the work before you hire.

The honest case for a fte

The strongest argument for in-house is long-term knowledge accrual. Someone who works at your company for years learns the business in a way a consulting team can't replicate in a quarter. If your roadmap is dense and the work isn't one-shot, that compounds. We don't dispute this — if the math is right, hire.

Werdykt

Gdzie pasujemy, a gdzie nie

Jesteśmy

Honest about when an in-house hire beats a consultancy engagement.

Nie jesteśmy

An evergreen vendor. When an in-house team is the right answer, the right answer is an in-house team.

Pytania

Pytania kupujących

What if i hire someone and they leave?

AI engineers are in demand, so budget for turnover. If your roadmap depends on a single named engineer staying, the risk is real. A written runbook and a trained team reduce it, and so does keeping managed AI operations running next to the hire.

Can Stennir help us decide whether to hire?

Yes. The AI opportunity audit ranks your workflows by value, risk and effort, with a build-and-run cost and a payback case for each. That is the input a hire needs: a clear picture of the work. The report is yours either way, so if the answer is an in-house team, you take the plan to it. We'd say so.

How should i compare Stennir with an in-house salary?

Stennir agrees scope and commercial terms in writing on a 30-minute call. The right comparison isn't list-price-to-list-price. It's total annual cost (salary + on-cost + manager time + tools + recruiting + idle time) vs the cost of the work you'll actually have Stennir ship.

Can we do both?

The most common pattern is: ship the first one with Stennir, then hire in-house once you know the shape of the org's AI work. The runbook from the first engagement is the in-house engineer's onboarding doc.

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