Every company considering AI eventually faces the same question: should we build it ourselves, buy a ready-made solution, or bring in a partner to build something custom?
The answer isn't always obvious, and getting it wrong is expensive. Building in-house when you should have bought wastes months of engineering time. Buying off-the-shelf when you need something custom means settling for a solution that doesn't quite fit. And choosing the wrong partner is arguably worse than either.
Here's a practical framework for making this decision, based on what we've seen work — and fail — across dozens of AI projects in European mid-market companies.
Option 1: Build in-house
When it works: You have an existing engineering team with ML experience, a unique data advantage that's core to your business, and the patience to invest 12-18 months before seeing production results.
When it fails: You're hiring your first ML engineer to "figure out AI." That person will spend six months understanding your business processes, three months experimenting, and then leave for a company that pays 40% more. You're back to zero.
The hidden cost of building in-house isn't the salaries — it's the opportunity cost. Every month your engineering team spends on AI infrastructure is a month they're not spending on your core product. For companies where AI is the core product, this makes sense. For companies where AI is a tool to improve operations, it usually doesn't.
Realistic cost for European mid-market: €200K-€500K/year in salaries alone (2-3 specialists), plus 6-12 months before first production deployment. Total first-year investment: €300K-€700K with uncertain outcomes.
Option 2: Buy off-the-shelf
When it works: Your need is generic enough that a product already exists. Email classification, basic chatbots, standard document OCR, meeting transcription. If thousands of other companies have the same need, someone has probably built a SaaS for it.
When it fails: Your processes are specific to your industry or your company. A generic tool handles 80% of the use case, but the remaining 20% — the part that actually differentiates your business — isn't covered. You end up paying for software that your team works around rather than with.
The other failure mode is integration. SaaS tools need to connect to your existing systems: your CRM, your ERP, your document management system. If those integrations don't exist or are poorly documented, you've just bought an island that doesn't connect to the mainland.
Realistic cost: €500-€5,000/month per tool. Low upfront investment, but costs compound when you need multiple tools, and you're locked into someone else's roadmap and pricing.
Option 3: Partner with a specialist
When it works: You need a custom solution that integrates deeply with your business processes, but you don't want to build and maintain an in-house AI team. You have clear processes that can be documented and improved. You want results in weeks, not years.
When it fails: You choose a partner who doesn't understand your industry, delivers a beautiful proof-of-concept that falls apart in production, or builds a system so complex that only they can maintain it — creating permanent dependency.
The key differentiator between a good AI partner and a bad one is what they deliver. Strategy firms deliver reports. Dev shops deliver code. A good implementation partner delivers working systems with knowledge transfer — you get production AI and your team learns enough to maintain and extend it.
Realistic cost: €30K-€150K for a focused implementation project. Higher upfront than SaaS, lower than building in-house, with the fastest time to production results.
The decision framework
Rather than defaulting to one option, run through these questions:
1. Is your need generic or specific?
If you can describe your AI need in one sentence that applies to any company in any industry — "we want to summarize meetings" or "we want to classify emails" — buy off-the-shelf. It's faster and cheaper.
If your need involves your specific data, your specific processes, or your specific industry knowledge, you need something custom (build or partner).
2. Is AI core to your product or a tool for operations?
If AI is your product — if your customers are paying for AI capabilities — build in-house. You need deep ownership of the technology.
If AI is a tool to make your existing business better — faster processing, fewer errors, lower costs — partner with a specialist. Don't divert your product engineering team from what they're actually good at.
3. How fast do you need results?
If you can wait 12-18 months: building in-house is viable.
If you need results in 6-12 weeks: partner with a specialist who has done this before.
If you need something yesterday: buy off-the-shelf and accept the tradeoffs.
4. What happens after launch?
AI systems aren't static. Models need monitoring, retraining, and updating as your data changes. Consider:
- In-house: Full control, but you need dedicated staff permanently.
- SaaS: The vendor handles maintenance, but you have no control over changes.
- Partner: Look for knowledge transfer during the project, with optional retainer support after launch.
The hybrid approach
In practice, most companies end up with a combination. You might use off-the-shelf tools for generic needs (meeting transcription, basic chatbots), partner with a specialist for your core business process automation, and gradually build internal capabilities as you learn what works.
The mistake is starting with the most complex option. Don't hire an ML team before you've proven that AI creates value for your business. Don't build custom when a €100/month SaaS solves 95% of the problem.
European-specific considerations
For European companies, the build-vs-buy decision includes additional factors:
Data residency. Many SaaS AI tools process data in US data centres. For companies handling sensitive European data, this may not be acceptable. Custom solutions can be deployed on European infrastructure or even on-premise.
GDPR compliance. Off-the-shelf tools may not offer the granularity of data processing controls that GDPR requires. Custom implementations can be designed for compliance from day one.
AI Act readiness. The EU AI Act introduces new requirements for high-risk AI systems. If your use case falls into a high-risk category (employment, credit scoring, certain healthcare applications), you need to ensure compliance — something that's easier to guarantee with a custom implementation.
Language requirements. Many off-the-shelf tools are optimised for English. If you need robust performance in French, Italian, German, or other European languages, you may need custom fine-tuning that only a partner or in-house team can provide.
Making the call
The right choice depends on your specific situation. But if we had to offer one rule of thumb for European mid-market companies: start with a focused partnership, then decide what to bring in-house based on what you learn.
A good implementation partner will help you understand not just the solution, but the problem. That understanding is what lets you make informed build-vs-buy decisions for every subsequent AI project.
Don't let the perfect be the enemy of the deployed.