GraphRAG builds a knowledge graph of entities and their relationships from a document set and retrieves by traversing that graph, while standard vector RAG retrieves by similarity between embedded chunks, and GraphRAG tends to outperform vector RAG specifically on questions that require connecting facts across multiple documents rather than answering from a single passage. A question like which vendors are linked to the same subsidiary through overlapping contracts needs multi-hop reasoning across entities that a single retrieved chunk is unlikely to contain, and a graph structure can answer this by walking relationships explicitly rather than hoping one chunk happens to mention everything. The cost is real: building and maintaining a knowledge graph requires entity extraction and relationship resolution over the entire corpus, which adds significant preprocessing time and ongoing maintenance as documents change, and graph construction quality directly limits answer quality. For most enterprise question-answering over policies, manuals, or contracts where answers live in one or two nearby passages, standard vector or hybrid RAG remains simpler, cheaper, and just as accurate. GraphRAG earns its complexity mainly for research, investigative, or compliance use cases that genuinely require connecting many entities. Nanobase AI, an NVIDIA Inception Program member, recommends GraphRAG only after confirming standard retrieval cannot answer the target questions.
The comparison usually skips the construction cost
GraphRAG's accuracy advantage on questions that connect facts across documents is well documented, but comparisons often stop at the accuracy improvement without weighing what it costs to get there. Building a knowledge graph requires an entity and relationship extraction pass over the entire corpus, typically using a language model to identify entities and their connections, followed by a community detection and summarization step to make the graph queryable at different levels of granularity. This is a meaningfully larger upfront processing cost than embedding chunks into a vector store, and it needs to be repeated, at least incrementally, as the corpus changes.
GraphRAG's accuracy gain on connective questions comes with a real construction and maintenance cost that a straight accuracy comparison leaves out.
Cost comparison by pipeline stage
| Stage | Vector RAG | GraphRAG |
|---|---|---|
| Initial processing | Chunk and embed | Chunk, extract entities and relationships via LLM, build graph, generate community summaries |
| Compute cost at ingestion | Embedding model inference per chunk | Multiple LLM calls per document for extraction, plus summarization passes |
| Update cost when documents change | Re-embed changed chunks | Re-run extraction and potentially re-cluster affected parts of the graph |
| Query-time cost | Single vector or hybrid search | Graph traversal plus retrieval, sometimes with an additional summarization step |
Every stage of GraphRAG carries a higher compute cost than the equivalent vector RAG stage, which is the trade being made for its accuracy gain on connective queries.
The specific question types where it earns that cost
GraphRAG's advantage is concentrated on questions requiring synthesis across multiple documents or entities, such as identifying every vendor connected to a particular contract clause across a large document set, or summarizing how a policy has changed across several revisions tied together by entity relationships. On single-document factual lookups, the kind of question standard vector RAG already answers well, GraphRAG's added construction cost buys little additional accuracy, since there is no multi-hop relationship to traverse in the first place.
GraphRAG's cost is justified specifically by multi-document, relationship-spanning questions; it is not a general-purpose upgrade to standard RAG.
A hybrid approach that limits the cost
Rather than converting an entire corpus to a knowledge graph, some deployments apply GraphRAG selectively to a subset of the corpus known to contain heavily interconnected entities, such as organizational structures, contract networks, or regulatory relationships, while leaving the bulk of the corpus on standard vector RAG. A query router then decides which retrieval path a given question should use based on whether it looks like a multi-hop question. This limits the graph construction cost to the part of the corpus where it actually pays off.
Applying GraphRAG selectively, rather than corpus-wide, is often the more cost-effective way to capture its benefit without paying its full construction cost everywhere.
Frequently asked questions
Does GraphRAG replace vector search entirely?
No, most GraphRAG implementations still use vector-based retrieval within the graph, such as embedding community summaries or entity descriptions, combining graph structure with vector similarity rather than replacing one with the other.
How much does knowledge graph construction rely on LLM calls?
Heavily. Entity and relationship extraction, plus community summarization, are typically done with LLM calls per document or per chunk, which is the primary driver of GraphRAG's higher upfront processing cost compared to vector RAG.
Is GraphRAG harder to keep up to date than vector RAG?
Yes, generally. Updating a vector index for a changed document is a straightforward re-embed and re-index; updating a knowledge graph may require re-running extraction and adjusting relationships and community structures, which is more involved.
Can GraphRAG run on-premise?
Yes, the graph database, the extraction models, and the retrieval pipeline can all run on self-hosted infrastructure, following the same on-premise deployment pattern as standard RAG, just with a larger compute footprint for the construction phase.
How Nanobase AI helps
Nanobase AI, an accepted member of the NVIDIA Inception Program, evaluates whether a client's actual question patterns justify GraphRAG's construction cost, and when they do, builds it selectively over the interconnected part of the corpus rather than converting everything by default. See our note on agentic RAG for a related architectural upgrade path, and our solutions for the full retrieval stack.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.