You Spent $35,000 Fine-Tuning a Model. A $28,000 RAG System Would Have Done It Better.

The most expensive mistake in enterprise AI right now is fine-tuning when retrieval is the actual answer.

The Decision That Costs More Than It Should

When an enterprise AI project needs domain-specific knowledge, two paths appear obvious. Fine-tune the model on your data. Or build a retrieval system that feeds the model your data at query time.

Most teams spend weeks debating the question. Then they choose wrong.

Over 70% of enterprise AI teams deploying LLMs in production use RAG as their primary knowledge-grounding technique. Fewer than 25% rely on fine-tuning as a standalone approach. The teams who tried fine-tuning first and switched to RAG learned something the hard way: fine-tuning solves a different problem than the one most enterprise teams actually have.