When does a knowledge graph actually improve RAG over pure vector retrieval?

We have a domain corpus with strong entity relationships — drugs, trials, pathways, researchers. We've seen demos where GraphRAG dramatically outperforms vector RAG on multi-hop reasoning questions. But implementing and maintaining a knowledge graph is significant engineering work. What are the real-world query types where the graph pays off vs. where plain vector retrieval is good enough?