When CTOs ask us 'how much does AI integration cost?' they usually have a rough idea about API pricing. But token costs are typically less than 20% of the total investment. Here's what you actually need to budget for when adding generative AI to your product or operations.
Development Costs: The Biggest Line Item
Building a production-ready AI feature is software engineering, not just API wiring. A basic AI chatbot with RAG typically takes 4-8 weeks of senior engineering time. A more complex system with AI agents, multiple data sources, and custom workflows runs 8-16 weeks. This includes architecture design, data pipeline development, prompt engineering, testing, and deployment. Expect to invest between €30,000 and €120,000 for the initial build, depending on complexity. The wide range reflects a real variance — a simple FAQ bot over your documentation is fundamentally different from a multi-agent system processing financial data.
Infrastructure & API Costs
LLM API costs vary dramatically by model and usage. GPT-4o costs roughly $2.50 per million input tokens and $10 per million output tokens. Smaller models like GPT-4o-mini or Gemini Flash are 10-20x cheaper. For a typical B2B application with a few thousand daily users, expect monthly API costs between €500 and €5,000. Add vector database hosting (€50-500/month depending on data volume), and any additional infrastructure for caching, queuing, and monitoring.
Data Preparation: The Hidden Cost
Your AI is only as good as the data it works with. Budget 20-30% of development time for data preparation: cleaning and structuring your knowledge base, designing chunking strategies, building ingestion pipelines, and creating evaluation datasets. Companies that skip this step end up with AI that gives impressive but wrong answers — which is worse than no AI at all.
Ongoing Maintenance
AI systems need ongoing attention. Models get updated (and sometimes deprecated), data sources change, user needs evolve, and edge cases surface in production. Budget for 10-20 hours per month of maintenance work, plus periodic larger updates. This covers prompt tuning, data pipeline updates, model evaluation, and performance optimization. Many companies underbudget here and end up with AI that degrades over time.
Cost Optimization Strategies
Smart architecture dramatically reduces ongoing costs. Use smaller, cheaper models for simple tasks (classification, extraction) and reserve expensive models for complex reasoning. Implement caching for repeated queries. Use streaming to improve perceived performance without increasing costs. Route requests to different models based on complexity. These optimizations can reduce API costs by 50-80% compared to naive implementations.
The Bottom Line
For a mid-complexity AI integration, budget €40,000-€80,000 for the initial build and €2,000-€8,000 per month for infrastructure and maintenance. The ROI typically becomes positive within 3-6 months if you're solving the right problem. We're happy to give you a more specific estimate for your use case — every conversation starts with a senior engineer who can assess your technical requirements honestly.
Ongoing Costs That Catch Teams Off Guard
Most teams budget for the initial build but underestimate ongoing costs. LLM inference costs can scale rapidly — a customer-facing chatbot handling 10,000 conversations per month might cost €500–2,000/month in API fees depending on the model and response length. Add monitoring, evaluation, and prompt iteration, and you're looking at 20–30% of the initial build cost annually for maintenance and optimization.
We help clients design systems that minimize ongoing costs without sacrificing quality. Techniques include intelligent caching of common queries, model routing (using cheaper models for simple tasks and expensive models only when needed), and prompt optimization to reduce token usage. These optimizations can reduce inference costs by 40–60% compared to a naive implementation.
Building vs. Buying: When to Use Off-the-Shelf AI
Not every AI feature needs to be custom-built. For standard use cases like text summarization, translation, or sentiment analysis, off-the-shelf APIs from OpenAI or Google are often sufficient and much cheaper than a custom solution. Custom development makes sense when you need deep integration with proprietary data, specific domain expertise, or control over the model's behavior and privacy. We help CTOs evaluate this build-vs-buy decision as part of our discovery process, saving weeks of exploration and often tens of thousands in unnecessary development costs.
