GraphRAG vs Standard RAG: Why Query Type Should Drive Your Choice

GraphRAG augments traditional retrieval-augmented generation by representing document content as a knowledge graph, allowing systems to answer multi-hop relational queries that standard vector similarity search cannot resolve. Unlike conventional RAG, which retrieves the most similar text chunks, GraphRAG traverses entity-relationship structures to answer questions about connections across a corpus. However, its advantages are largely limited to relational queries; if such queries make up less than 15% of production traffic, the indexing costs — Microsoft's 2024 implementation ran to $33,000 for large datasets — outweigh the benefits. Recent cost-reduction strategies, including selective extraction and hybrid NLP-plus-LLM pipelines, have brought expenses down by 10 to 90 percent depending on corpus type. Experts recommend auditing your query distribution before committing to GraphRAG, treating it as a third retrieval tool alongside vector and BM25 search rather than a wholesale replacement.