Build vs. Buy: The Crucial Comparison for AI Integration
Many software providers face a strategic crossroads: Should they develop AI features themselves or rely on whitelabel solutions? Both paths have their justification – the right choice depends on individual factors.
Option 1: In-house Development – Complete Control with High Effort
Developing custom AI solutions costs between 45,000 and over 450,000 euros, depending on complexity and scope.
The Factors:
Personnel Resources: AI specialists in the DACH region demand between 180 and 320 euros per hour. Building an internal AI team requires months of recruiting and continuous investments in highly qualified personnel.
Time Investment: Custom AI development takes 6 to 18 months, which can delay the product's time to market. Additionally, time for research, recruiting, and testing needs to be factored in.
Ongoing Costs: Annual maintenance costs range from 10 to 30 percent of the original development budget. AI models require continuous updates for optimal performance and quick troubleshooting, when needed.
Hidden Expenses: Data preparation, computing power, and GDPR compliance are often underestimated and drive overall costs and durations up.
High Flexibility: With custom AI solutions, companies have control over every last detail of how the product should ultimately look. This can be particularly helpful for very specific use cases or large database systems.
Complications: In large projects, complications can always arise. Particularly, IT projects exceed their budgets on average by 45 percent and often deliver only 60 percent of the promised value. (Source)
When does Build make sense? When there are very specific requirements that no whitelabel solution can fulfill, when there's strategic differentiation through proprietary AI algorithms, or when long-term independence is the top priority.
Option 2: Whitelabel AI Integration – Fast Start with Proven Technology
AI integration through white-label platforms typically costs between 9,000 and 90,000 euros – significantly lower than the costs of in-house development and also feasible for SMEs.
The Factors:
Speed: Whitelabel solutions allow for a launch in days to weeks, instead of months. The product is already in most cases available and only needs to be integrated into the company’s data and software structure. However, this factor should be planned for with enough time and resources.
Limited Customization Framework: Whitelabel solutions are existing products and therefore offer only limited opportunities for customizing the product itself. If very specific functions are needed, this can be a disadvantage.
Access to Expertise: Specialized platforms offer ready-to-use, proven technology with continuous updates without incurring your own maintenance and development costs. This also eliminates months of in-house research.
Resource Focus: Companies can continue to focus on their core competencies instead of building AI infrastructure. The necessary expertise for the AI product is sourced externally and can be applied directly.
Branding Flexibility: 73 percent of companies prioritize complete customization options. Modern whitelabel platforms offer UI control, custom domains, and rebrandable APIs.
When does Buy make sense? For standardized AI functions, tight budgets and timelines, lack of internal AI expertise, or when fast market entry is essential.
The Direct Comparison: Build vs. Buy
In-house Development:
Initial Investment: 90,000 to 450,000 euros
Time-to-Market: 6 to 18 months
Break-Even: 24 to 48 months
Maximum Adaptability and IP Control
Full GDPR Control over Data Processing
High Resource Requirement for Maintenance
White-Label Integration:
Initial Investment: 9,000 to 90,000 euros
Time-to-Market: A few days to weeks
Break-Even: 2 to 4 months
Proven Technology with Lower Risk
GDPR-compliant European providers available
Maintenance and troubleshooting are usually included
The Decision Matrix: Which Path is Right for You?
Building your own AI product makes sense if:
You have highly specialized, unique AI requirements
Your business model is based on proprietary AI technology
You require complete control over your AI infrastructure in the long term
Sufficient budget and time resources are available
Maximum data sovereignty and GDPR control are required
A whitelabel AI solution makes sense if:
You need market-ready AI functions quickly
Standardized AI applications meet your needs
Limited AI expertise exists within the team
You want to minimize development risks
Focus is on the core business rather than technology development
GDPR-compliant European whitelabel providers are available
Conclusion: The Individual Decision Matters
There is no universal answer to the build vs. buy question. While particularly large companies with many resources prefer to retain full control with their own AI solution, smaller software providers often rely on whitelabel AI integration for faster ROI, lower risk, and better planning.
The best strategy: Analyze your individual requirements, resources, and strategic goals. In comparison, you will usually quickly notice which solution is best suited for your company. And if you choose whitelabel integration, our Scavenger Expert Team is always available for a no-obligation conversation.