AI Implementation Comparison
Build vs Buy: In-House AI Team vs Implementation Partner
Should you hire AI engineers and build internally, or partner with specialists? Here's how to decide.
Quick Answer
Build in-house if AI is your core product and you need continuous iteration. Partner with specialists if AI enhances your operations but isn't your primary business—you'll get to production faster and avoid the overhead of recruiting scarce talent.
True Cost Comparison
Building In-House (Year 1)
- Senior ML Engineer (1)€90-150K
- Data Engineer (1)€70-110K
- Recruiting costs (20% salary)€30-50K
- Infrastructure & tooling€20-40K
- Ramp-up time (3-6 months)Opportunity cost
- Total Year 1€210-350K+
Implementation Partner (Typical Project)
- AI Audit & roadmap2-4 weeks
- Implementation6-12 weeks
- Training & handoverIncluded
- Time to production8-16 weeks total
- InvestmentCustom-scoped
When to Build In-House
If you're building an AI-powered product for customers, the capability needs to be core to your organization.
Daily model updates, constant experimentation, and tight integration with product development favor in-house teams.
Building a team takes time. Expect 6+ months before meaningful output, 12+ months before the team is fully effective.
When to Partner with Specialists
If you're using AI to automate internal processes, improve customer service, or enhance decision-making, you don't need a permanent team.
Partners bring proven patterns and skip the learning curve. What takes a new team 6 months might take specialists 6 weeks.
Competition for ML engineers is fierce. A partner sidesteps the recruiting challenge entirely.
Partners have delivered similar projects before. They know what works and what doesn't. You benefit from their pattern recognition.
The Hybrid Approach
Many companies start with a partner to get initial AI systems into production, then build internal capability over time. This approach:
- • Delivers quick wins while you recruit
- • Provides a working reference implementation for new hires
- • Reduces risk of bad early decisions
- • Lets you validate AI ROI before committing to headcount
The Bottom Line
For most mid-market companies, partnering with AI specialists delivers faster results at lower risk. Building in-house makes sense when AI is your core business, not when it's enhancing operations. The hybrid approach—partner first, build later—often offers the best of both worlds.