GraphRAG vs Vector RAG: When to Add Knowledge Graphs to Your Enterprise Pipeline
Vector retrieval struggles with multi-hop questions across thousands of documents. Here is a working framework for deciding when a knowledge graph earns its cost.
Standard retrieval falls apart when the question requires connecting dots across documents. Ask a vector-only system to summarize supply chain risk across 5,000 vendor contracts, and it will return the 20 chunks nearest the query embedding. The other 4,980 contracts never enter the context window. The model writes a confident answer from a narrow slice.
Vector search finds specific needles. It does not describe how the needles are arranged.
That is the real decision in front of you. Not GraphRAG versus Vector RAG as a tribal choice, but whether the questions your users ask are single-hop lookups or multi-hop traversals. The honest answer is often: both, in one pipeline.
Where vector-only retrieval breaks
A standard RAG pipeline chunks documents, embeds each chunk as a vector, and at query time returns the chunks nearest the query vector. For "What is the penalty clause for late delivery in the Acme Corp contract?", this works. The query vector lands next to the clause.
Production queries rarely look like that. A compliance lead asks: "Which of our European logistics vendors are exposed to the new data privacy rules, and what are their liability caps?"
That requires several hops. Identify European logistics vendors. Pull their master service agreements. Find the liability caps. Cross-reference against the regulatory definitions. Vector similarity does not execute that sequence. It retrieves chunks that look textually similar to the query, which is not the same as chunks that are relationally relevant.
An important honest note: for most single-hop questions, a well-tuned hybrid search (BM25 plus dense vectors, with a strong reranker) matches or beats GraphRAG at a fraction of the cost. Microsoft's own GraphRAG paper shows the gains concentrate on global sensemaking and multi-hop queries, not on classic lookup. Do not add a graph to solve a problem your retrieval settings can solve.
What GraphRAG actually changes
GraphRAG shifts work to ingestion. Instead of only embedding chunks, an LLM reads the documents and extracts entities (nodes) and relationships (edges) into a structured schema. A contract becomes [Acme Corp] → (SUBCONTRACTS_TO) → [Beta Logistics], and so on.
At query time, the system can traverse edges rather than guess by similarity. Microsoft's GraphRAG research demonstrated meaningful gains on global, dataset-wide questions over naive RAG.
The production pattern is hybrid. Qdrant's write-up on graph RAG describes embedding the graph's nodes and summaries as vectors, using semantic search to find entry points, then walking edges for relational context. You get both pathways.
Keep the pre-computed community summaries. During indexing, the system clusters related nodes and writes a summary per cluster. At query time, broad "what does this corpus say about X" questions hit a summary instead of a slow live traversal. This moves latency off the user's wait and into the background.
The costs you actually pay
Vector-only RAG is cheap to build. Embedding models cost cents per million tokens. You can index a mid-sized corporate archive for the price of lunch.
GraphRAG is not that. Entity extraction runs a capable LLM over every token of your corpus. The dominant costs:
- ▸Extraction pass. You pay normal LLM input and output pricing for every token, times however many documents you have. Check your provider's current pricing page; rates for capable models shift quarterly.
- ▸Entity resolution. The raw extraction will produce
[Acme Corp],[Acme Corporation], and[Acme]as three nodes. You need another pass (or a deterministic resolver) to merge them. Skip this and the graph is noise. - ▸Community detection and summarization. Another LLM pass to cluster and summarize.
- ▸Deltas. If your corpus changes daily, you pay these costs on the delta forever.
A rough estimate you can run yourself: documents × avg_tokens_per_document × passes × provider_rate_per_token. Three to four passes is normal for a production-grade graph. Plug in your own numbers before approving a budget. If the extraction model is a small open-weight model served on your own GPUs, the per-token figure collapses to amortized compute, which is usually the only way to make a high-volume, frequently updating graph pencil out.
Entity resolution is where most quality problems live. LLM-extracted graphs need QA: sampled review of merged entities, dedup accuracy, edge precision. Without that loop, the graph degrades silently and the retrieval quality follows.
Decision framework
| Architecture | Mechanism | Indexing cost | Where it fits |
|---|---|---|---|
| Hybrid search (BM25 + dense + rerank) | Semantic plus keyword, reranked | Very low | Handbooks, SOPs, support, most single-hop lookup |
| Pure GraphRAG | Entity/edge extraction, graph traversal | High | Fraud rings, ownership structures, intelligence analysis |
| Hybrid Graph + Vector | Vector entry points, graph traversal, community summaries | High | Legal discovery, supply chain risk, cross-document regulatory review |
Two heuristics for the decision:
- ▸If users ask single-document, fact-lookup questions, start with tuned hybrid search. Measure. Do not add a graph on vibes.
- ▸If users ask "across all our vendors / contracts / filings, what pattern holds", vector top-k will miss by design. A graph plus community summaries is the right spend.
Production failure modes
Schema collapse. Demo pipelines let the LLM extract any entity it likes. You end up with nodes for [Tuesday] and [The Meeting]. Define a strict schema up front: [Vendor], [Contract], [Regulation], [Clause], [Jurisdiction], and reject the rest. This single discipline separates a working graph from an unmanageable one.
Latency under multi-hop queries. A six-hop traversal plus generation can run 15 to 30 seconds (illustrative). That is a product-killer in an interactive UI. Pre-compute community summaries during ingestion so broad queries read a summary instead of walking the graph live.
Entity resolution drift. Without a human QA sample on merged entities, duplicates and false merges compound. Budget for review.
Stale deltas. Document updates must map back to the specific nodes and edges they produced, so you can delete and re-extract cleanly. Metadata discipline from day one, or orphaned nodes forever.
Over-reliance on the graph. Not every query should traverse. Route single-hop queries to vector search and reserve graph traversal for the questions that need it. Rigorous evaluation tells you which is which.
Frequently asked questions
Does GraphRAG replace our vector database? No. Production systems run both. The vector store embeds nodes, edges, and community summaries so semantic search can find a graph entry point. Then the graph handles traversal. Qdrant alongside a graph store, or a vector store that holds graph structures natively, both work.
Is GraphRAG always better than hybrid search? No. On single-hop questions, a well-tuned hybrid retriever with a strong reranker often matches or beats GraphRAG for a fraction of the cost. The graph earns its keep on multi-hop and global-sensemaking queries. Test both on your real questions before committing.
Can we use open-weight local models for extraction? Yes, and for high-volume or frequently updating corpora this is the only sustainable path. Fine-tune a smaller open-weight model on your extraction schema and serve it with vLLM or SGLang. You trade per-token API cost for predictable compute. See our on-prem speed guide for throughput tuning.