AI Implementation Comparison
Build vs Buy: In-House AI Team or Implementation Partner?
Should you hire AI engineers and build internally, or work with a specialized partner? This guide breaks down the true costs and tradeoffs.
Quick Answer
Build in-house if AI is your core product and you can invest 12+ months before seeing returns. Partner with a specialist if you need AI to improve existing operations and want production results in weeks, not years.
The True Cost Comparison
Building In-House (Year 1)
- ML Engineer (senior)€90-150K
- Data Engineer€70-110K
- Recruiting & onboarding€30-50K
- Infrastructure & tools€20-40K
- 6-12 month ramp-upOpportunity cost
- Year 1 total€210-350K+
Working With a Partner
- AI Audit2-4 weeks
- Implementation6-12 weeks
- Team trainingIncluded
- Time to production8-16 weeks total
- Total investmentFraction of in-house cost
When to Build In-House
If you're building an AI-first product company, you need in-house expertise. Your competitive advantage depends on it.
Building a competent AI team takes time. Recruiting, onboarding, and the first failed experiments are part of the process.
If your AI needs to evolve daily based on user feedback, an internal team provides the tightest feedback loop.
When to Partner
You're not building an AI product—you're using AI to make your operations faster, cheaper, or more accurate.
A partner with experience can deploy production AI in 8-16 weeks. An internal team needs 6-12 months just to get started.
Partners have done this before. They know the failure modes, the shortcuts that don't work, and the patterns that do.
An AI audit identifies the highest-ROI opportunities before you commit resources. Build confidence before building systems.
The Hybrid Approach
Many of our clients start with a partner engagement and eventually build internal capabilities. This is often the smartest path:
- • Partner implements first AI systems, proving value
- • Your team learns by working alongside the implementation
- • Knowledge transfer builds internal capability
- • Retainer support as you transition to self-sufficiency
The Bottom Line
Building in-house makes sense when AI is your core business. For everyone else, partnering gets you to production faster, at lower cost, with less risk. And you can always build internal capabilities later, informed by real production experience.