What Three AI Staffing Models Cost (With Our Numbers Included)

We charge €10K-€28K per month for dedicated AI teams. Here's how that compares to building internally and hiring a consultancy, with the math laid out so you can check it.

We're one of the three options in this comparison, so our incentive is obvious. We've tried to make the numbers transparent enough that you can adjust the inputs and reach your own conclusions. Where we've used our own client data, we say so. Where we've used third-party sources, we cite them.

Model 1: Building an internal AI team

This is the right choice for some companies. It's also the most frequently underestimated in total cost.

Salary costs in Western Europe

A senior ML engineer in Germany earns a median base of roughly €100,000 per year, with the 75th percentile around €110,000. In France, the median sits closer to €80,000. The UK falls between the two. These are base salary figures from Glassdoor and Levels.fyi as of early 2026.

For a production AI project (the kind that goes beyond a prototype and actually runs in a business environment), you typically need at minimum: one senior ML engineer, one mid-level ML or data engineer, and one software engineer for integration work. That's a base salary bill of roughly €240,000-€300,000 per year in Western Europe, depending on location and seniority mix.

Employer costs on top of salary

In Germany, mandatory employer contributions (social insurance, pension, health) add roughly 20-21% to base salary. In France, the figure is closer to 40-45%. Add benefits (conference attendance, training budget, equipment) and you're looking at a loaded cost multiplier of 1.3x to 1.5x depending on country. That €270,000 base salary bill becomes €350,000-€400,000 in total employer cost per year for three engineers.

Hiring costs

Recruiter fees for specialized ML roles run 15-25% of first-year salary. For three hires at an average base of €90,000, that's €40,000-€67,000 in one-time recruitment costs. Internal recruiting reduces this but shifts the cost to your HR team's time, which has its own opportunity cost.

The time cost

This is the number most internal business cases undercount. The median time to hire an ML engineer in Germany is 32 days from posting to offer acceptance, according to Agency Partners' 2025 analysis of the German market. But that's posting-to-offer. Add the notice period (1-3 months is standard in Germany), onboarding (the commonly cited 90-day ramp to productivity), and the reality that you're hiring sequentially rather than in parallel (because each hire influences the next), and you're looking at 5-9 months before a three-person team is fully staffed and productive.

During that ramp period, the project is either waiting or being staffed with people who aren't yet effective. In a business case that assumed a six-month delivery timeline, a nine-month hiring ramp means the project starts late and the ROI calculation shifts by at least a year.

Retention risk

ML engineers in Europe change jobs frequently. Gartner's 2024 survey reported that only 29% of IT workers express high intent to stay with their current employer. When a senior ML engineer leaves after 18 months, you absorb not just the replacement cost (another €30,000-€65,000 in recruiter fees, another 3-6 month ramp) but the knowledge loss from the person who understood your data, your models, and your production environment.

What this adds up to for year one

For a three-person internal AI team in Western Europe, a reasonable total cost estimate for the first year (salary, employer costs, recruiting, equipment, training, management overhead) is €420,000-€520,000. That number drops in year two once recruiting costs are absorbed, but only if nobody leaves.

Model 2: Engaging a management consultancy or Big 4 firm

This is the choice for companies that want brand-name risk cover and can pay for it.

Day rates

Big 4 firms and tier-1 management consultancies charge €2,000-€3,500 per day per consultant for AI and data science work in Europe. Boutique AI consultancies sit lower, typically €1,200-€2,000 per day. These figures come from 2025 pricing guides by Leanware and industry benchmarks compiled by Futuriom and Orient Software.

What a project actually costs

A typical AI proof-of-concept engagement through a Big 4 firm involves a team of 2-4 consultants for 8-12 weeks. At an average blended rate of €2,500 per day per consultant, a three-person team for 10 weeks (50 working days) costs €375,000. That's for a PoC. Taking the same project through development, validation, and production deployment with a consultancy typically means 4-8 months of engagement at similar or higher rates, reaching €600,000-€1,200,000 for a single project delivered to production.

What you get for the money

Consultancies provide structured methodology, executive-facing deliverables, and organizational credibility. A McKinsey or Deloitte engagement carries weight in board presentations that an internal team or a 15-person Lithuanian AI shop (like us) does not. This has real value for companies navigating internal politics around AI investment.

What you don't get

You typically don't get the same engineers throughout the project. Consultancy staffing models rotate junior consultants through engagements, with senior partners appearing for steering committees but not writing code. The people who understand your data in month two may not be the people working on your project in month four.

You also don't usually get IP ownership. Many consultancy contracts retain co-ownership or licensing rights over frameworks, tools, and sometimes models developed during the engagement. This varies by firm and is negotiable, but it's a default you need to negotiate away rather than a default you receive.

When this model makes sense

If the primary goal is organizational change management alongside AI delivery (getting a 50,000-person organization to adopt AI workflows), consultancies bring capabilities that small technical teams don't. If the primary goal is building and deploying a specific AI system, you're paying for overhead that doesn't translate into working code.

Model 3: Renting a dedicated AI team

This is what we sell, so read accordingly.

Our pricing

AAI Labs offers four tiers, published on our website:

Starter: €10,000 per month. Two AI engineers. Suited for feasibility assessments, proofs of concept, and tightly scoped integrations.

Build: €14,000 per month. One team lead plus four AI engineers. Suited for taking a validated concept through development to production.

Scale: €21,000 per month. One team lead plus six AI engineers. Suited for enterprise-grade implementations requiring ongoing operations across multiple systems.

Configure: €28,000 per month. Custom team composition for projects with specific expertise requirements or larger scale.

These are flat monthly fees. No per-hour billing, no variable rates, no separate charges for project management.

What a project actually costs

A typical production AI project runs 4-8 months. Using the Build tier (the one we deploy most frequently) for six months: €14,000 × 6 = €84,000. That buys a team lead and four engineers working in two-week sprints with biweekly deliverables, peer-reviewed code, and model evaluation against hold-out data.

For comparison: that same €84,000 covers roughly 34 consultant-days at a Big 4 firm. At a Big 4, 34 days of a single consultant won't get you past the scoping phase of most AI projects. At our rates, it pays for five people for six months.

What you get

Dedicated engineers who stay on your project for its duration (not a rotating bench). Full IP ownership of all code, models, and outputs. Knowledge transfer documentation and training sessions at the end of the engagement. Sprint-based accountability with defined deliverables.

What you don't get

We don't carry the brand credibility of McKinsey or Deloitte. If your CEO needs a Big 4 logo on the AI strategy presentation to get board approval, we're not the right choice. We also don't provide the deep organizational change management that large consultancies specialize in. We build AI systems. We don't restructure your organization around them.

We also don't provide indefinite continuity at the same cost advantage. As we discussed in our rent vs. build post, if a project extends to 2-3 years of continuous work, permanent hires become more cost-effective. We're priced for bounded engagements, not permanent staffing.

Side-by-side comparison

Here's how the three models compare for a representative project: building a production AI system over six months with a team of approximately four engineers.

The price differential between the models is large enough that it's worth pausing on. We cost roughly 80% less than a Big 4 engagement and roughly 80% less than a first-year internal build for a comparable team size. That gap reflects three things: our engineers are based in regions with lower salary costs (primarily Lithuania and East Africa), we carry lower overhead than global consultancies, and we don't bill for partner time, bench costs, or brand premium.

Whether that price difference translates into value depends on what you need. If you need organizational transformation with AI as one component, the Big 4 model is better despite the cost. If you need a permanent AI capability that grows with the company, the internal build is better despite the speed penalty. If you need a specific AI system built and deployed within a defined timeline, with full IP ownership and sprint-based accountability, the dedicated team model is more cost-effective by a wide margin.

The numbers we'd want to see if we were buying

If we were on the other side of this decision, here's what we'd want to verify before committing to any of the three models:

For an internal build: What's the actual time-to-productive-team in your industry and region? (Ask your HR team for their last three technical hires, measure from req opening to first productive sprint.) What's your 18-month ML engineer retention rate? (If you don't know, assume 50-60%.) What management overhead does the AI team require? (If the answer is "our VP of Engineering will manage them alongside the rest of the team," that VP's time has a cost.)

For a consultancy: What percentage of billed hours come from engineers with more than two years of ML production experience? (Ask for CVs of the actual project team, not the pitch team.) Who owns the IP? (Read the MSA, not the summary.) What's the cost of change orders if scope shifts? (It will shift.)

For a dedicated team (including ours): What's the team's attrition rate during engagements? (If engineers rotate mid-project, you lose context.) What does the handover process look like, and what's its cost? (Ask for examples from past clients.) How does the team handle domain expertise they lack? (If the answer is "we'll figure it out," that's a risk.)

The honest version

Every staffing model has a scenario where it wins and a scenario where it fails. The internal build wins on continuity and institutional knowledge but loses on speed and upfront cost. The consultancy wins on organizational credibility and change management but loses on cost-effectiveness for technical delivery. The dedicated team wins on speed, cost, and IP clarity but loses on brand credibility and long-term continuity.

We've made our pricing transparent because we think the comparison favors us for a specific type of engagement: bounded, technically defined projects where the goal is a working AI system in production within 3-12 months. If that describes your situation, the math points in our direction. If it doesn't, one of the other models is probably a better fit, and we'd rather you make that call with clear numbers than buy from us for the wrong reasons.

AAI Labs provides dedicated AI engineering teams at published, flat monthly rates. We work in 2-week sprints with full IP ownership for the client. See our pricing and team configurations or get in touch.