Custom LLM Development in Helsinki: When to Build vs. Buy

Off-the-shelf LLM tools cover 60% of use cases. The remaining 40% is where custom development pays off — and where most of the competitive advantage sits.

Three patterns to consider

We compare three patterns: prompt engineering on top of a hosted model, retrieval-augmented generation (RAG) on your own documents, and fine-tuning. Each has very different cost, latency, and data-residency implications.

When RAG is the right answer

If your data must stay inside the EU and you handle sensitive customer information, a private RAG deployment is usually the right answer — reasonable cost, high accuracy, and full control.

When to fine-tune

Fine-tuning shines for tone, formatting, or domain-specific terminology — not for teaching new facts. Reach for it after RAG is in place, not before.