Vector retrieval is the default for enterprise knowledge AI. For most use cases, it works. But for content where the relationships between entities are the meaningful structure — legal contracts referencing other contracts, financial filings citing prior filings, engineering documents that depend on parts catalogues — pure semantic similarity misses the structure that matters. This is a practical guide to GraphRAG, the hybrid pattern that fits most enterprises, and the cost realities behind both.
Where vanilla RAG underperforms
Vector search finds chunks that are semantically similar to your query. That is the entire mechanism. Three query patterns expose its limits, and they show up the moment a real legal or finance team starts asking real questions:
- Multi-hop questions: "what indemnification clause governs Project X?" — vector retrieves chunks that loosely mention all three, not the right one
- Aggregation queries: "how many open positions report to a VP-level manager?" — requires traversal, not similarity
- Entity-resolution queries: "summarize what we know about Acme Corp" — needs everything connected to that entity, not chunks that mention it
What GraphRAG adds
A knowledge-graph layer on top of (or alongside) the vector store. Documents go through an entity-extraction pass that identifies people, organizations, contracts, products — and the relationships between them. The graph then lets queries traverse: find the contract, find clauses referencing party X, return the matching chunk. The LLM gets richer context — not just semantically similar text, but text connected by structured relationships.
Three domains where it earns its cost
Legal: contract Q&A across portfolios of thousands of agreements with cross-references — clauses referencing master agreements, schedules referencing exhibits, amendments updating earlier versions. Finance: audit and disclosure traceability across filings, where one statement implies another and the chain matters as much as the content. Engineering: parts and system documentation where a question about an assembly requires walking the bill of materials and a question about a subsystem requires resolving parent components.
Hybrid is the realistic answer
GraphRAG is not a replacement for vector search; it is an augmentation. Most production systems keep vector as the default retrieval (cheap, fast, broad recall) and trigger graph traversal for queries the planner identifies as relationship-heavy. The query router decides per request — the user sees one search experience, two retrieval paths run underneath. The router is usually a small classifier or a rule-based scorer; the cost of running it is dwarfed by the cost of running every query through the graph.
Cost and operational realities
GraphRAG roughly doubles indexing cost (you are running entity extraction in addition to embeddings) and adds graph-storage overhead. Query latency depends on traversal depth. Maintenance is more complex — your entity schema needs to evolve with your business. Most enterprises don't need GraphRAG for everything; they need it for one or two domains where vector search demonstrably fails. The honest evaluation: build a test set of 50 questions from real users, score vanilla RAG against it, then score hybrid retrieval against it, and decide on the delta.
IDS AI Solutions builds hybrid RAG architectures as part of the Enterprise RAG & Knowledge Systems service — a Knowledge Retrieval Audit scores vanilla RAG vs. GraphRAG against a sample of your team's actual questions, so the decision rests on data, not marketing. Talk to our team for a tactical evaluation.