You’re facing one of the most crucial strategic decisions of the coming years: Which AI components should you develop in-house, and which should you buy?
Your answer will determine millions of euros, years of development time, and, ultimately, your competitive edge. Yet most companies make this decision based on gut feeling—a costly mistake.
In practice, companies that systematically weigh in-house development against external procurement often deliver their AI projects faster and at a lower total cost.
The decision is complex, because AI isn’t a single, monolithic technology. A customer support chatbot requires completely different approaches than a machine learning system for optimizing your production.
This article provides a structured foundation for your decision—practical and to the point, with no marketing jargon.
What Does Make or Buy Mean for AI Components?
In the context of AI, make or buy goes far beyond the classic “build or buy” question.
For AI systems, you need to decide across several architectural layers: foundation model, application logic, data infrastructure, and user interface.
The Four Levels of AI Decision-Making
Foundation Models: Here, the choice is usually obvious—you buy. Whether GPT-4, Claude, or Gemini: Training your own large language model costs millions and, for most companies, simply isn’t worth it.
Application Logic: This is the heart of your AI solution. Here you decide whether your system merely runs standard workflows, or truly sets you apart from competitors.
Data Infrastructure: Vector databases, ETL pipelines, monitoring systems. Often underestimated, but crucial for scalability and performance.
User Interface: There are countless chat interfaces available off the shelf. Custom input screens tailored to your workflows, however, are a rarity.
Hybrid Approaches as the Norm
In real-world scenarios, purely make-or-buy decisions are rarely optimal. Successful companies combine both approaches in smart ways.
They use external APIs for core AI functions but develop their own application logic to suit specific requirements. The result? Rapid time-to-market, with full control over what makes them unique.
But beware the hubris trap: many teams overestimate their abilities and underestimate the complexity. A ChatGPT wrapper does not constitute an AI strategy.
Technical Decision-Making Factors in Detail
Existing IT Infrastructure
Your existing infrastructure is the biggest cost driver—or cost saver—in any AI project.
On-premises systems often require elaborate integration. Cloud-native businesses, on the other hand, can scale quickly. That said, legacy systems are not automatically a deal-breaker for in-house development.
The crucial factor is the API capability of your current systems. Modern APIs allow for elegant integration; outdated interfaces force expensive workarounds.
Realistic Assessment of In-House Capabilities
Do you have the right people? This question will determine your project’s success or failure.
AI development requires far more than Python skills. You’ll need data scientists, ML engineers, DevOps specialists, and domain experts—a rare combination.
| Competency | Fit for Make | Buy Alternative |
|---|---|---|
| ML/AI Engineering | High (if available) | External Development |
| Domain Expertise | Very High | Hard to Replace |
| Data Management | Medium | Cloud Services |
| DevOps/MLOps | Low | Managed Services |
Reality check: Can you fund a full AI team for at least two years? If not, there are many arguments in favor of external partners or out-of-the-box solutions.
Security and Compliance
Data protection is non-negotiable—but it doesn’t have to kill innovation.
GDPR and industry-specific regulations create clear guardrails. Cloud solutions often offer higher security standards than in-house systems—if properly configured.
The key is data classification: Which data can be processed externally? Which must remain in-house? This determines your architectural choices.
Scalability and Performance
AI workloads are unpredictable. A viral chatbot can overload your infrastructure within hours.
Cloud services provide elastic scaling—with corresponding costs. Own systems give you control but require careful capacity planning.
The rule of thumb: For unpredictable spikes in demand, cloud APIs are superior. For steady, high volumes, in-house systems often pay off.
Economic Evaluation Criteria
Calculating Total Cost of Ownership Accurately
The real costs often hide in details your CFO won’t notice until later.
Development costs are only the beginning. Maintenance, updates, compliance, monitoring, and support all drive up the TCO. With cloud services, you pay continuously; with in-house development, hidden follow-up costs can multiply.
A real-world example: An internal chatbot costs €150,000 to develop, but requires €80,000 per year for operations and enhancements. After three years, that’s €390,000—with no guarantee of updates or new features.
Making Return on Investment Measurable
You can measure AI ROI—if you define the right metrics.
Avoid soft KPIs like “improved user experience.” Focus on hard facts: hours saved, processing times reduced, conversion rates increased.
An example from manufacturing: Automated quote generation cuts the time needed from 8 to 2 hours per quote. At 200 quotes a year, that’s 1,200 hours saved—at €80 per internal hour, that’s €96,000 saved annually.
Risk Allocation: Make vs. Buy
Both approaches come with different risks—how much are you willing to tolerate?
Make Risks: Technology obsolescence, staff turnover, budget overruns, security gaps. In return: full control and independence.
Buy Risks: Vendor lock-in, price hikes, service outages, data privacy breaches. In return: predictable costs and professional support.
The smart strategy: Diversify risks. Build your critical core functions in-house; outsource standard processes.
Financing Models and Budget Planning
AI projects often fail due to inflexible budget planning.
In-house development requires high up-front investments. Cloud services work on a subscription basis. Hybrid models combine both approaches.
For midsized businesses, the “Start small, scale smart” principle is often best: Start with cloud services, gain experience, then decide on in-house development down the track.
Industry-Specific Considerations
Mechanical Engineering and Industry 4.0
In industry, make-or-buy decisions often hinge on highly specialized requirements.
Production optimization requires a deep process understanding. Off-the-shelf AI tools don’t know why your CNC machine behaves differently with certain materials. This is where in-house development pays off.
Document automation, on the other hand, is standardized. Quotes, specification sheets, and maintenance reports follow similar patterns—regardless of the manufacturer.
SaaS and Digital Service Providers
SaaS companies often have ideal conditions for in-house AI development: cloud-native infrastructure, agile teams, and a data-driven culture.
However: your core competency is your product, not AI research. Use available APIs for standard features; only build what truly sets you apart.
A practical tip: A/B testing different AI services helps with your decision. Which works better—GPT-4 or Claude for your specific use case?
Traditional Service Providers
Consultancies, law firms, and agencies face special challenges: legacy systems, regulatory requirements, and cautious leadership structures.
A step-by-step approach often works best here. Start with safe, clearly scoped use cases. An internal knowledge chatbot, for example, is less of a risk than automated customer consulting.
Field-Tested Decision Scenarios
Scenario 1: Customer Support Automation
Thomas, from manufacturing, wants to automate spare parts support. Eighty percent of inquiries are standard questions about delivery times and compatibility.
Make Option: In-house development with a RAG system and proprietary spare parts database. Cost: €200,000, 8 months of development.
Buy Option: Chatbot-as-a-Service with API integration. Cost: €1,500 monthly, 4-week setup.
Recommendation: Buy for the initial phase, make for advanced features. The chatbot first collects data on common queries—these insights later improve the in-house solution.
Scenario 2: Document Automation
Anna from SaaS wants to automatically personalize onboarding materials, so every new client receives tailored guides.
Make Option: Template engine with LLM integration and customer data pipeline. Effort: €120,000, 5 months.
Buy Option: Document generation API with custom templates. Cost: €800 monthly per 1,000 documents.
Recommendation: Hybrid approach. Use external APIs for standard templates and develop custom adaptations in-house.
Scenario 3: Predictive Maintenance
Markus wants to predict failures in the IT infrastructure. The challenge: 15 different legacy systems with different data formats.
Make Option: Custom ML system with integrations for all legacy systems. Effort: €350,000, 12 months.
Buy Option: Enterprise monitoring with AI features. Cost: €3,000 monthly, 6 weeks integration.
Recommendation: Step-by-step approach. Immediately implement standard monitoring; later develop custom ML for critical systems.
Framework for Making the Right Choice
The Brixon Decision Tree
Systematic decisions require structured frameworks. This checklist helps you evaluate objectively:
- Strategic Relevance: Is this AI function critical for your core business, or is it a commodity?
- Differentiation Potential: Will building in-house truly set you apart?
- In-House Skills: Do you have the necessary skills—or can you acquire them quickly?
- Time Pressure: How soon do you need to deliver?
- Budget Flexibility: Can you afford large up-front investments?
- Data Sovereignty: Do sensitive data need to stay in-house?
- Scalability Requirements: Are workload spikes predictable?
Applying the Evaluation Matrix
Rate each factor from 1 to 5. A score above 25 suggests “make,” below 15 suggests “buy,” and anything in between calls for a hybrid approach.
But beware the illusion of mathematical precision: The framework offers guidance, but not the absolute truth. Intuition and experience still matter.
Choosing the Right Time to Decide
Many companies decide too early or too late. The ideal time is after the proof-of-concept phase.
Only once you truly understand what your AI application needs to achieve, can you make an informed make-or-buy decision. Theoretical assessments often lead you astray.
Conclusion and Recommendations
The make-or-buy decision for AI is more complex than for traditional software—but it can be solved systematically.
Successful companies take it step by step: They start with cloud services, gain experience, and then develop strategically important components themselves.
This approach minimizes risks and maximizes learning. You avoid both the vendor lock-in trap and the “in-house development hubris.”
Your next step: Identify a concrete use case and work through the decision framework. Seek expert advice, but make your own call.
AI is too important for your business to leave this decision to chance.
Frequently Asked Questions
When should midsize companies develop AI components themselves?
In-house development is worthwhile when three factors come together: the AI function is business-critical, you have the right skills in your team, and the application provides real competitive advantage. For standard use cases like chatbots or document processing, cloud services are usually more efficient.
How high are the hidden costs of in-house AI development?
Expect to spend 60–80% of the initial development cost annually on maintenance, updates, and operations. A system with €150,000 in development costs will require roughly €90,000–120,000 a year to run—excluding major feature updates.
What AI expertise does a company need for in-house development?
A complete AI team needs data scientists, ML engineers, DevOps specialists, and domain experts. At least four full-time professionals over two years—which amounts to around €800,000–1,200,000 in personnel costs. Smaller teams can develop individual components, but not entire AI systems.
Are cloud AI services GDPR-compliant?
Yes, if you configure them properly. Look for EU hosting, data processing agreements, and explicit GDPR compliance from your providers. Many cloud services meet higher security standards than internal systems—the key is correct implementation.
How do I objectively assess ROI for AI projects?
Focus on measurable KPIs: hours saved, reduced turnaround times, increased conversion rates. Avoid soft factors like “improved user experience.” A realistic ROI timeframe for AI projects is 18–36 months.
What’s the best starting point with AI for traditional companies?
Begin with a clearly scoped, low-risk use case, such as an internal knowledge chatbot or automated document creation. Use cloud services for proof of concept and gain experience before committing to larger investments.
