Fine-tuning vs. RAG — when does fine-tuning actually win?

We keep reading that RAG is the right default and fine-tuning is for style/format, not knowledge. But we've had cases where a fine-tuned model on domain-specific data outperformed a RAG pipeline with the same data. What are the actual decision criteria practitioners use?