You are facing one of the most important strategic decisions of the coming years: Which AI components will you develop in-house, and which will you buy?
The answer will decide over millions of euros, years of development time, and ultimately your competitive edge. Yet, most companies make this choice based on gut feeling – an expensive mistake.
What we observe: Companies that weigh up in-house development versus purchasing systematically often realize their AI projects faster and with lower overall costs.
The decision is complex, because AI is not a uniform technology. A chatbot for customer support has completely different requirements than a machine learning system for optimizing your production processes.
This article provides you with a structured, practical decision-making basis – no marketing buzzwords, just hands-on guidance.
What does Make or Buy mean for AI components?
In the context of AI, Make or Buy goes far beyond the classic question of «develop in-house or purchase».
With AI systems, you decide about several architectural layers: the foundation model, the application logic, the data infrastructure and the user interface.
The four decision levels
Foundation Models: Here, the decision is usually clear – you buy. Whether it’s GPT-4, Claude or Gemini: training your own large language models costs millions and makes little sense for most companies.
Application logic: The heart of your AI solution. Here you determine whether your system simply reproduces standard workflows or actually creates competitive differentiation.
Data infrastructure: Vector databases, ETL pipelines, monitoring systems. Often underestimated, but crucial for scalability and performance.
User interface: There is an abundance of chat interfaces. Special input masks tailored to your workflow, on the other hand, are rare.
Hybrid approaches as the standard
In practice: Pure make-or-buy decisions are rarely optimal. Successful companies combine both intelligently.
They use external APIs for basic AI functions, but develop the specific application logic themselves. The result: fast time to market with full control over differentiation.
But beware of the hubris effect: many teams overestimate their abilities and underestimate the complexity. A ChatGPT wrapper is not an AI strategy.
Technical decision factors in detail
Existing IT infrastructure
Your current infrastructure is the biggest driver or saver of costs in AI projects.
On-premise systems often require complex integration. Cloud-native companies, on the other hand, can scale quickly. But even here: legacy systems don’t automatically exclude in-house development.
The key factor is the API capability of your legacy systems. Modern APIs allow elegant integration—outdated interfaces force costly workarounds.
Assess internal skills honestly
Do you have the right people? This question decides success or failure.
AI development needs more than just Python skills. You need data scientists, ML engineers, DevOps specialists and domain experts—a rare combination.
| Expertise | Make suitability | Buy alternative |
|---|---|---|
| ML/AI Engineering | High (if available) | External development |
| Domain expertise | Very high | Hard to substitute |
| 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 for external partners or off-the-shelf solutions.
Security and compliance
Data protection is non-negotiable—but it does not have to kill innovation.
The GDPR and industry-specific regulations provide clear frameworks. Cloud solutions often offer higher security standards than internal systems—if correctly configured.
The decisive factor: data classification. What data may external systems process? Which must remain internal? This distinction defines your architecture decisions.
Scalability and performance
AI workloads are unpredictable. A viral chatbot can overload your infrastructure in hours.
Cloud services offer elastic scalability—for a price. In-house systems give you more control, but require careful capacity planning.
Rule of thumb: for unpredictable load spikes, cloud APIs have the edge. For constant, high volumes, your own systems are often more cost-efficient.
Economic evaluation criteria
Calculating Total Cost of Ownership correctly
Real costs are often hidden in details that your CFO only discovers later.
Development costs are only the beginning. Maintenance, updates, compliance, monitoring and support all drive the TCO up. With cloud services, you pay continuously; with in-house development, hidden follow-on costs often explode.
A realistic example: An internal chatbot costs 150,000 euros to develop, but 80,000 euros per year for operation and further development. After three years, you’re at 390,000 euros—without guarantees for updates or new features.
Making Return on Investment measurable
AI ROI can be measured if you define the right metrics.
Avoid soft indicators like «improved user experience.» Focus on hard facts: working hours saved, lead times reduced, conversion rates increased.
Practical example from mechanical engineering: Automated quote creation reduces processing time from 8 to 2 hours per quote. At 200 quotes per year, that’s 1,200 hours saved—at an internal hourly rate of 80 euros, that equals 96,000 euros saved annually.
Risk distribution between Make and Buy
Both approaches carry different risks—know your tolerance.
Make risks: Technology obsolescence, staff drop-out, budget overruns, security gaps. In return: full control and independence.
Buy risks: Vendor lock-in, price hikes, service outages, data privacy violations. In return: predictable costs and professional support.
Smart strategy: diversify risks. Develop critical core functions in-house, outsource standard processes.
Financing models and budgeting
AI projects often fail due to inflexible budgeting.
In-house development requires large up-front investments. Cloud services work like a subscription. Hybrid models combine both approaches.
For mid-sized companies, the «Start small, scale smart» principle is recommended: start with cloud services, gain experience, and then decide about in-house development.
Industry-specific characteristics
Mechanical engineering and Industry 4.0
In industry, domain-specific requirements often determine whether to make or buy.
Production optimization needs deep process understanding. Standard AI tools don’t understand why your CNC machine behaves differently with certain materials. Here, in-house development pays off.
Document automation, on the other hand, is easily standardized. Quotes, specifications and maintenance reports follow similar patterns—no matter the manufacturer.
SaaS and digital service providers
SaaS companies often have the best conditions for in-house AI development: cloud-native infrastructure, agile teams and a data-driven culture.
Still: Your core expertise is your product, not AI research. Use available APIs for standard features, develop only what creates real differentiation.
Tip from practice: A/B tests with different AI services help with the 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.
Here, the step-by-step approach is often optimal. Start with secure, clearly scoped use cases. An internal chatbot for company knowledge is less risky than automated customer consulting.
Proven decision scenarios from practice
Scenario 1: Customer support automation
Thomas from mechanical engineering wants to automate spare parts support. 80 percent of queries are standard questions about delivery times and compatibility.
Make variant: Internal development with a RAG system and your own spare parts database. Cost: 200,000 euros, 8 months’ development.
Buy variant: Chatbot-as-a-service with API integration. Cost: 1,500 euros per month, 4 weeks set-up.
Recommendation: Buy for the start, make for advanced features. Initially, the chatbot gathers data on frequent queries—these insights later improve in-house development.
Scenario 2: Document automation
Anna from SaaS wants to automatically personalize onboarding materials. Each new customer receives tailored guides.
Make variant: Template engine with LLM integration and customer data pipeline. Effort: 120,000 euros, 5 months.
Buy variant: Document generation API with custom templates. Cost: 800 euros per month per 1,000 documents.
Recommendation: Hybrid approach. Use external APIs for standard templates, develop specific customizations in-house.
Scenario 3: Predictive maintenance
Markus wants to predict outages in IT infrastructure. The challenge: 15 different legacy systems with different data formats.
Make variant: Own ML system with custom integrations for all legacy systems. Effort: 350,000 euros, 12 months.
Buy variant: Enterprise monitoring with AI features. Cost: 3,000 euros per month, 6 weeks integration.
Recommendation: Gradual approach. Immediately implement standard monitoring, develop custom ML for critical systems later.
Framework for the right choice
The Brixon decision tree
Systematic decisions need structured frameworks. This checklist helps you evaluate objectively:
- Strategic relevance: Is this AI function critical to your core business or just a commodity?
- Differentiation potential: Does in-house development actually create competitive advantage?
- Internal skills: Do you have – or can you quickly build – the necessary skills?
- Time pressure: How quickly do you need to deliver?
- Budget flexibility: Can you cover high up-front investments?
- Data sovereignty: Must sensitive data remain internal?
- Scalability requirements: Are the load peaks predictable?
Applying the evaluation matrix
Rate each factor from 1 to 5. A score over 25 suggests Make, under 15 Buy, in between speaks for hybrid approaches.
But beware of pseudo-mathematical accuracy: the framework provides guidance, not the final truth. Gut feeling and experience are still crucial.
Timing the decision
Many companies decide too early or too late. The optimal moment is after the proof-of-concept phase.
Only once you understand what your AI application is really supposed to do can you make a well-founded make-or-buy decision. Theoretical assessments often lead astray.
Conclusion and recommendations for action
The make-or-buy decision for AI is more complex than for traditional software—but also systematically solvable.
Successful companies proceed in stages: they start with cloud services, gather experience, and then develop strategically important components themselves.
This strategy minimizes risks and maximizes learning effects. You avoid both the vendor lock-in trap and the in-house development hubris.
Your next step: Identify a concrete use case and apply the decision framework. Seek expert advice, but make your own decision.
AI is too important for your business to leave the decision to chance.
Frequently Asked Questions
When should medium-sized companies develop AI components themselves?
In-house development is worthwhile when three factors coincide: The AI function is business-critical, you have the necessary skills in your team, and the application delivers a real competitive edge. For standard applications like chatbots or document processing, cloud services are usually more efficient.
How high are the hidden costs for in-house AI development?
Plan for 60-80 percent of the original development costs annually for maintenance, updates and operation. A system with development costs of 150,000 euros will need around 90,000-120,000 euros annually for ongoing operation—without major feature updates.
What AI skills 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 positions over two years—that’s around 800,000-1,200,000 euros in personnel costs. Smaller teams can develop individual components, but not complete AI systems.
Are cloud AI services GDPR-compliant?
Yes, if you configure the services correctly. Pay attention to EU hosting, data processing agreements, and explicit GDPR compliance by your providers. Many cloud services offer higher security standards than internal systems—a correct implementation is key.
How do I objectively evaluate the ROI of AI projects?
Focus on measurable KPIs: working hours saved, reduced processing times, increased conversion rates. Avoid soft factors like «improved user experience.» A realistic ROI window for AI projects is 18–36 months.
What is the best way for traditional companies to get started with AI?
Start with a clearly defined, low-risk use case like an internal chatbot for company knowledge or automated document creation. Use cloud services for the proof of concept and gain experience before making larger investments.
