Honest comparison for companies considering whether to build AI capability with internal team or hire a specialised boutique. When each option makes sense, real total cost figures and the hybrid model that works in most cases.
Each row is a real measurable criterion. We mark with ★ where each option wins. When in-house team / in-house build wins, we say so.
| Criterion | Datalvar AI | in-house team / in-house build |
|---|---|---|
| Time to production (first initiative) | 2-3 months | 6-12 months |
| Year 1 cost (for 3-5 initiatives) | €80-250k (project + operation) | €350-600k (2-3 people full-time loaded) |
| Focus during project | 100 % | Fragmented by changing priorities |
| Initial expertise available | High from day 1 | Depends on profiles hired; learning curve |
| Discontinuity risk | Low (acotated contract) | High (AI rotation in Spain 25-40 %/year) |
| Direction change with new model version | Weeks | Requires internal training + evaluation |
| Internal politics sensitivity | Low (external) | High (dependencies, priorities, budget) |
| Long-term knowledge retention | Requires structured handover at end | ★Native (stays in team) |
| Capacity to scale initiatives | Limited to contracted scope | ★Scales with hiring |
| Alignment with internal culture | Low initially | ★High |
| Marginal cost of additional initiative | High (new project) | ★Low (team already exists) |
We publish this list so you decide with criteria. If we don\'t fit, we save you and us time.
Fintech with 60 people considered hiring 2 ML engineers and 1 data engineer (~€380k/year loaded cost). Opted for pilot with us: €45k, 3 months, validated ROI. Then hired 1 ML engineer to operate and evolve. Year 1 saving: ~€280k.
Had tried in-house build (2 years, no production). Us: recommender pilot in 10 weeks + handover to their existing data engineer. Scaled by their internal team with occasional support from us.
Large company with AI CoE of 8 people uses us for urgent or specialised initiatives that don't fit their internal roadmap. Model: we execute, their team audits and operates post-delivery. Complementary, not substitute.
Real loaded cost of a minimum functional AI team in Spain (1 senior ML engineer + 1 data engineer + 0.5 DevOps + fractional management): between €280k and €450k/year. Senior AI profiles salaries €65-110k gross + 30-35% social cost + tools (licenses, cloud, monitoring, evals) €40-80k/year.
This cost doesn't include continuous training, conferences, rotation (rehiring and onboarding an ML engineer who leaves costs ~€30k in productivity loss + search), or management time dedicated to the initiative.
A mature AI CoE (5-8 people + tools + operations) usually sits at €600k - €1.2M/year. It's a serious investment — justify the decision with expected initiative volume and organisational maturity.
Senior AI freelancers in Spain charge between €90 and €180/hour. A project requiring 400 h of senior costs between €36k and €72k just in execution. Plus management, coordination, tools and responsibility.
It's a valid option for very acotated punctual needs (evaluating a model, doing a proof of concept), but hard to scale. Without a structured team, end-to-end project responsibility dilutes.
We and other boutiques compete with this model by providing structure, end-to-end responsibility, guarantees and predictable total cost.
We design from the start for handover. All code, prompts, architecture and documentation are yours. At project end we deliver structured training (4-12 sessions) to the people who will operate the solution.
We always recommend a hybrid mid-term model: we launch the first 2-4 initiatives while you hire and train 1-2 internal profiles. At 12-18 months, your internal team can maintain and evolve the system with occasional support from us.
It's not in our interest that you depend eternally. A boutique's reputation is built with clients who grow and recommend, not with trapped clients. We work so that in 18-24 months you can dispense with us if you want.
It's the most common situation. Many internal teams start with energy and stall due to: lack of specific production expertise (not research), changing priorities fragmenting focus, senior staff rotation, or operational load devouring innovation time.
In that scenario we help as external technical partner: we launch concrete initiatives in parallel while your internal team maintains what already works. Typical model: we execute, your team audits, learns and takes over operation post-delivery.
It's a win-win model: your internal team gains speed and learning, and we work with a client who values specialisation.
Free 30-min diagnosis. No hard sell — if in-house team / in-house build fits your project better, we\'ll tell you.