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The Complete 2026 Guide for European Companies
Real pricing data from dozens of AI projects. No fluff, no "it depends" without context. Actual numbers you can use in your business case.
Before diving into details, here is the big picture. AI project costs vary dramatically based on scope, complexity, and whether you are building from scratch or integrating existing models.
| Project Type | Cost Range (EUR) | Timeline |
|---|---|---|
| Proof of Concept (PoC) | 15,000 - 25,000 | 2-4 weeks |
| MVP / Pilot | 25,000 - 60,000 | 4-8 weeks |
| Production Deployment | 30,000 - 200,000 | 2-6 months |
| Enterprise Platform | 100,000 - 500,000+ | 6-12 months |
| Monthly Retainer | 3,000 - 15,000/mo | Ongoing |
Typical PoC cost for a well-scoped AI project. This validates whether AI can solve your specific problem before you invest in production.
Pro tip: A good PoC should answer one question: "Can AI solve this problem well enough to justify production investment?" If your vendor is building a PoC that costs more than EUR 25K, they are probably building an MVP.
Production deployments require scalable infrastructure, monitoring, security, and compliance — all of which add cost beyond the core model work.
System design, API contracts, infrastructure planning
ETL, data quality, preprocessing, embeddings
Fine-tuning, prompt engineering, evaluation
API development, existing system connections
Unit tests, integration tests, adversarial testing
CI/CD, logging, alerting, model monitoring
Technical docs, user guides, team training
AI systems are not "set and forget." They need ongoing maintenance, monitoring, and optimization. Here is what typical retainer packages look like:
20-30 hrs/month
30-60 hrs/month
60-100 hrs/month
The eternal question. Here is a factor-by-factor comparison to help you decide:
| Factor | Build Custom | Buy Off-the-Shelf |
|---|---|---|
| Time to Market | 3-12 months | 1-4 weeks |
| Upfront Cost | EUR 50K-500K | EUR 1K-10K/month |
| Customization | Unlimited | Limited to vendor features |
| Data Privacy | Full control | Depends on vendor |
| EU AI Act Compliance | Your responsibility | Shared with vendor |
| Maintenance Burden | High (your team) | Low (vendor handles) |
| Scalability | Architecture dependent | Usually built-in |
| Vendor Lock-in Risk | None | High |
| Competitive Advantage | Unique IP | Same as competitors |
| Total Cost (3 years) | EUR 150K-800K | EUR 36K-360K |
Bottom line: Buy when the problem is well-understood and commoditized. Build when AI is your competitive advantage, you have strict data privacy requirements, or off-the-shelf solutions do not fit your workflow.
Validate the business case with a EUR 15-25K proof of concept before committing to a EUR 100K+ production build. Most AI projects that fail do so because the problem was wrong, not the technology.
GPT-4, Claude, and Gemini are good enough for 80% of use cases. Fine-tuning a custom model costs 3-5x more and rarely delivers proportional value improvement.
Do not use GPT-4 when GPT-4o-mini works. Smaller models are 10-50x cheaper per token and often perform comparably for routine tasks. Use large models only for complex reasoning.
Cache LLM responses for repeated queries. A good caching strategy can reduce API costs by 40-70% for systems with repetitive patterns.
Clean data reduces the iteration cycles needed. Every dollar spent on data preparation saves three to five dollars on model development and debugging.
Open-source models (Llama, Mistral, Qwen) eliminate per-token API costs. Combined with efficient serving frameworks (vLLM, TGI), you can cut inference costs by 70-90%.
Document Q&A, knowledge base search, internal wiki assistant
Autonomous task execution, multi-step workflows, tool-using agents
Voice assistants, phone bots, real-time transcription systems
Automated compliance checks, document review, risk classification
Every project is different. Use our AI Scope Generator to get a tailored estimate based on your specific requirements, timeline, and budget constraints.