When a company decides to build an AI system, the technical decisions usually get made fast. The staffing decision, how you actually assemble the people who will build it, often doesn't get the same scrutiny, even though it's frequently the bigger cost driver. There are broadly three ways to do it: hire internally, bring in a management consultancy or Big 4 firm, or rent a dedicated external team. Each has a real cost structure, and the numbers are worth putting side by side before you commit.
Model 1: Building an internal AI team
A production-ready AI project generally needs at least three people: a senior ML engineer, a mid-level ML or data engineer, and a software engineer to handle integration work. In Western Europe, a senior ML engineer's base salary runs around €100,000 in Germany (median, with the 75th percentile closer to €110,000) and around €80,000 in France, with the UK typically landing between the two. A team of three at those levels puts base salaries alone at roughly €240,000 to €300,000 a year.
Base salary is only part of the bill. Mandatory employer contributions add roughly 20-21% in Germany and 40-45% in France, and once you factor in benefits, training, and equipment, the real multiplier lands around 1.3x to 1.5x of base pay. That puts total first-year employer cost for three engineers around €350,000 to €400,000, before recruiting.
Recruiting itself isn't free either: specialized ML recruiters typically charge 15-25% of first-year salary, which for three hires averaging €90,000 works out to roughly €40,000 to €67,000 in one-time fees (or an equivalent amount of internal HR time if you handle it yourself).
Then there's the time it takes to actually get productive. Median hiring time in Germany runs around 32 days from job posting to offer acceptance, notice periods commonly run one to three months, and onboarding to full productivity is often cited around 90 days. Add it up, and a realistic timeline to a fully productive team is five to nine months, which quietly turns a planned six-month project into a nine-month one and pushes ROI out by at least a year.
Retention is the other risk worth pricing in. Gartner's 2024 research found only 29% of IT workers express high intent to stay with their current employer. If a senior engineer leaves 18 months in, you're looking at another €30,000 to €65,000 in replacement recruiting, another three to six months of ramp-up, and real knowledge loss on the data, models, and production environment they understood.
All in, a realistic year-one cost for a three-person internal AI team lands around €420,000 to €520,000, including salaries, employer costs, recruiting, equipment, training, and management overhead. Year two is cheaper if nobody leaves.
Model 2: Management consultancy or Big 4 firm
Day rates here vary by tier: Big 4 firms typically bill €2,000 to €3,500 per consultant per day, while boutique AI consultancies run €1,200 to €2,000. A fairly typical proof of concept, two to four consultants over eight to twelve weeks, at a blended rate of around €2,500 a day for a three-person team over ten weeks (50 working days), comes out to roughly €375,000. Carry that through a full project lifecycle, development, validation, and production, and the total commonly lands between €600,000 and €1,200,000.
What you're paying for is real: a structured methodology, executive-facing deliverables, and organizational credibility that matters in board presentations, especially for large-scale change management across big organizations. What's often less visible upfront is that the same engineers aren't necessarily on the project throughout, junior consultants tend to rotate while senior staff appear mainly at steering points, IP ownership can end up shared or co-owned by default, and code-writing consistency across project phases isn't guaranteed. This model tends to make the most sense when organizational change management matters as much as the technical delivery, and less sense when the goal is simply a specific AI system built and shipped.
Model 3: Renting a dedicated external team
The third option is a flat-fee dedicated team, the model behind AAI Labs' own AI Team For Hire offering, so it's worth being upfront that we have a stake in how this comparison reads. The published tiers for that offering look like this:
| Tier | Monthly cost | Composition | Best for |
|---|---|---|---|
| Starter | €10,000 | Two AI engineers | Feasibility assessments, PoCs, scoped integrations |
| Build | €14,000 | One team lead + four AI engineers | Validated concept through production deployment |
| Scale | €21,000 | One team lead + six AI engineers | Enterprise implementations across multiple systems |
| Configure | €28,000 | Custom composition | Specific expertise or larger scale needs |
Billing is a flat monthly fee: no hourly rates, no variable pricing, no separate project management line item. A six-month project on the Build tier comes to €14,000 × 6, or €84,000, roughly equivalent to 34 Big 4 consultant-days, which wouldn't even cover project scoping under that model.
The tradeoffs run the other way, too. This model works well for bounded, technically defined projects where the goal is a working system in production within roughly three to twelve months. It doesn't carry the brand weight of a Big 4 name in a board presentation, it isn't built for deep organizational change management, and beyond two to three years, a well-retained permanent hire usually becomes more cost-efficient than an ongoing external engagement.
Putting the numbers side by side
For a six-month production AI project needing roughly four engineers: an internal build runs around €210,000 to €260,000 for that period, a Big 4 engagement runs €600,000 to €900,000, and a dedicated rented team runs closer to €84,000. Time to a productive start follows a similar pattern: five to nine months for internal hiring, two to four weeks for a consultancy, and days for a pre-assembled external team.
Each model also carries its own hidden costs. Internal builds carry retention risk and the opportunity cost of a slow start. Consultancies carry the risk of junior staff billed at senior rates, scope rigidity, and change-order fees. External dedicated teams carry the risk of a knowledge-transfer gap if the handover isn't planned properly, and dependence on the external team's domain familiarity.
Questions worth asking before you commit to any of these
If you're leaning toward an internal build: what's the actual time-to-productive-team for your last few technical hires, what's your realistic 18-month retention rate for ML engineers (assume 50-60% if you don't know), and who's absorbing the management overhead?
If you're leaning toward a consultancy: what percentage of billed hours will come from people with real production ML experience, who owns the IP (read the actual services agreement, not the summary), and what does a scope change cost?
If you're leaning toward a dedicated external team: what's their attrition rate mid-engagement, what does handover actually look like and cost, and how do they handle a domain they haven't worked in before?
There's no universal right answer here
Every one of these models wins in some situations and loses in others. Internal teams win on continuity and institutional knowledge, and lose on speed and upfront cost. Consultancies win on organizational credibility and change management, and lose on cost-efficiency for pure technical delivery. Dedicated external teams win on speed, cost, and IP clarity, and lose on brand weight and long-term continuity. The right call depends on your timeline, your budget, and whether the job is bounded and well-defined or open-ended and organization-wide.
If your situation looks like a bounded, technically defined project with a clear production target, it's worth running the numbers on a dedicated team model like ours, and we're happy to walk through how that would look for your project. If it looks more like a long-term capability you want to own in-house, or a large-scale change effort, one of the other two models is probably the better fit, and that's a reasonable conclusion to reach.