Microsoft Research presents LazyGraphRAG, a novel approach to Retrieval-Augmented Generation (RAG) that blends vector and graph-based search methods. This method aims to enhance AI's ability to answer questions about private datasets by leveraging relationships within unstructured text. LazyGraphRAG offers a cost-effective alternative to traditional GraphRAG by deferring LLM use until query time, avoiding costly up-front data summarization. It balances "best-first" (vector RAG) and "breadth-first" (GraphRAG) search strategies for both local and global queries. Testing demonstrates that LazyGraphRAG achieves comparable or superior performance to other RAG methods at a significantly lower cost. The approach will be integrated into the open-source GraphRAG library and further improvements will be explored, although traditional GraphRAG still has its advantages.