In 2026, a serious AI build runs roughly €16,000 to €56,000 per month of development, depending on the use case: internal copilots sit at the low end, RAG knowledge systems in the middle, and agentic workflows or custom-model work at the top. The monthly band matters more than a fixed project quote, because AI products are iterated into existence, not specified up front.
Cost bands by use case
| Use case | Monthly band | Time to first production version | Typical team |
|---|---|---|---|
| Internal copilot / assistant | €16-24k | 6-10 weeks | 1 senior AI engineer + part-time product |
| RAG knowledge system | €20-32k | 8-14 weeks | 1-2 AI engineers + data engineer (part-time) |
| Agentic workflow (tool use, multi-step) | €28-44k | 10-16 weeks | 2 senior AI engineers + eval focus |
| Custom model / fine-tuning platform | €40-56k | 12-20 weeks | 2-3 engineers incl. ML platform |
What actually moves the number
- Data readiness: messy, scattered source data adds weeks of pipeline work before AI work starts, the most common budget overrun.
- Evaluation depth: anything user-facing or agentic needs real eval suites; skipping them is cheaper for a month and far more expensive after.
- Integration surface: each system the AI must read from or act on adds cost; agents multiply this.
- Reliability bar: an internal tool at 90% usefulness is cheap; a customer-facing feature at 99% is not the same project.
- Team model: embedded senior engineers at transparent rates typically land these bands; agency builds add scoping and management margin on top.
How to budget it
- 1Pick one use case and model its value first, cost only means something against payback.
- 2Budget monthly, not fixed-bid: commit to 3 months, evaluate against defined metrics, then extend or stop.
- 3Reserve 15-30% of build cost per month for run: inference, monitoring, evals and iteration after launch.
- 4Start at the smallest band that can prove value; copilot-first beats platform-first for most teams.
