GraphRAG vs Vector RAG: How to Choose the Right Architecture for Enterprise AI

GraphRAG is the most important advancement in enterprise retrieval architecture since hybrid search — but most teams reach for it too soon or skip it entirely when it would solve their hardest retrieval failures. The core idea: instead of storing your documents as embedding vectors in a flat index, GraphRAG extracts entities and relationships from them, builds a knowledge graph, and uses graph traversal alongside vector similarity to answer questions that require connecting facts across multiple documents.

Vector RAG works well for the majority of enterprise queries — self-contained factual lookups against a document corpus. It fails on multi-hop questions that require reasoning across relationships: which customers of Company X are affected by this policy change, how does this drug interact with this treatment protocol given a patient's comorbidities, what is the chain of organizational approvals for this contract type. These are exactly the high-value questions that justify deploying AI in the first place. On enterprise benchmarks, GraphRAG achieves 86% accuracy on multi-hop tasks where vector RAG scores 32% — a 54-percentage-point gap. Our RAG architecture guide for enterprise production covers the full retrieval stack; this post goes deeper on when and how to add the graph layer.

This guide covers the architecture difference between GraphRAG and vector RAG, the query distribution that determines which retrieval path to use for each request, a production implementation walkthrough, and the governance considerations — indexing cost, latency, and schema management — that determine whether GraphRAG is worth the operational overhead. For selecting the right vector store to pair with GraphRAG, see our guide to enterprise vector database selection.

What Is GraphRAG and How Does It Differ from Vector RAG?

Standard vector RAG encodes each document chunk as an embedding vector and retrieves the top-k chunks with highest cosine similarity to the query embedding. The model then generates an answer from the retrieved chunks. This pipeline is fast, relatively cheap to operate, and handles the majority of enterprise retrieval tasks well. Its fundamental limitation is that embedding similarity finds chunks that look like the query — it cannot follow relationships between entities across chunks or reason about the graph of connections in your data.

GraphRAG adds a structured knowledge graph to the retrieval layer. During an offline indexing phase, an LLM extracts entities — people, organizations, products, concepts, dates — and the relationships between them from your document corpus. Those entities and edges are stored in a graph database. A community detection algorithm, typically the Leiden algorithm, groups entities into thematic clusters. The LLM then generates a natural language summary of each community. At query time, the retrieval layer can traverse the graph — following entity relationships through multiple hops — instead of relying solely on embedding similarity.

• →Vector RAG: embed query → cosine similarity search → top-k chunks → LLM generation. Single retrieval step, fast, works well for factual and semantic lookups.
• →GraphRAG local search: entity lookup from the query → graph traversal across related entities → combined context from traversed nodes and associated chunks → LLM generation. Answers relationship queries by following edges the vector index cannot express.
• →GraphRAG global search: uses pre-computed community summaries to answer thematic, cross-corpus questions — questions that require synthesizing information across the entire document collection rather than drilling into specific chunks.
• →Hybrid retrieval: both vector similarity search and graph traversal run in parallel, results are merged with a reranker, and the combined context goes to the LLM. This is the production standard for most enterprise deployments in 2026.

Where Vector RAG Fails: The Multi-Hop Query Problem

Multi-hop queries require following a chain of relationships to arrive at an answer. A simple factual lookup ('What is our refund policy?') is answered by finding the relevant policy chunk. A multi-hop query ('Which enterprise customers are on the legacy billing plan and are affected by the Q3 price change?') requires connecting customer account data to billing plan data to the price change policy document — following a chain of entity relationships that no single chunk contains. Vector RAG retrieves the three most similar chunks to the query embedding. It does not connect them.

• →Policy synthesis: 'Which contracts contain indemnification clauses that conflict with our updated liability policy?' — requires connecting contract entities to clause entities to policy documents across potentially hundreds of documents.
• →Compliance gap analysis: 'Which of our AWS deployments in the EU region are not compliant with our data residency controls?' — requires traversing service → region → control → policy relationships.
• →Organizational reasoning: 'Who are the engineers who worked on both the payment service and the authentication module and are still at the company?' — pure entity relationship queries that embedding search cannot resolve.
• →Incident root cause: 'Which alert fired upstream of the incident at 14:32 UTC and which services depend on it?' — requires following causal relationship chains through an observability entity graph.

The pattern is consistent: the answer exists in your documents, but it is distributed across entity relationships that a flat vector index has no way to express. GraphRAG's knowledge graph stores those relationships explicitly and traverses them at query time.

The 80/15/5 Rule: Matching Query Complexity to Retrieval Strategy

Prism Labs' analysis of enterprise RAG deployments shows a consistent query distribution: roughly 80% of enterprise queries are simple semantic lookups best served by fast vector search, 15% require graph traversal to answer relational or multi-hop questions, and 5% need full agentic treatment with multi-step planning and tool use. This distribution has a critical implication: if you deploy pure GraphRAG, you pay a latency and cost penalty on 80% of your workload for no retrieval quality gain. If you deploy pure vector RAG, you fail on the 15% of queries that are often the highest business-value ones.

The right production architecture is not GraphRAG or vector RAG — it is an intelligent router that dispatches each query to the appropriate retrieval path based on query complexity classification. A lightweight query classifier sits in front of your retrieval infrastructure and routes each request to vector search, graph traversal, or hybrid retrieval based on detected complexity. Production teams report 95% classification accuracy with classification latency under 75ms — negligible against full request latency.

How GraphRAG Works: From Documents to Knowledge Graph

GraphRAG implementation has two phases: an expensive offline indexing phase that builds the knowledge graph once, or incrementally as documents are added, and a query phase that uses it at runtime. Understanding the indexing phase is critical because it determines both cost and the quality ceiling of your retrieval — the graph can only answer questions about entities and relationships it successfully extracted.

• →Entity and relationship extraction: an LLM processes each document chunk and extracts named entities (organizations, people, products, dates, technical components) and the relationships between them as triples — (Company X, acquired, Company Y), (Service A, depends_on, Service B). This is the most expensive step: it requires one LLM call per chunk and must produce structured output at scale. A 1M-token corpus at $0.003 per 1K tokens input costs approximately $3,000 in extraction using a mid-tier model.
• →Entity resolution: the same real-world entity often appears under multiple names across documents. Entity resolution merges 'AMZN', 'Amazon', and 'Amazon Web Services parent' into a single graph node using string matching combined with semantic similarity scoring.
• →Community detection: the Leiden algorithm runs on the entity-relationship graph to identify clusters of densely connected entities. A community might correspond to your payments domain, your infrastructure layer, or a specific product line. Communities become the unit of global search.
• →Community summary generation: the LLM generates a natural language summary of each community using its entities, relationships, and associated document chunks as context. These summaries answer thematic cross-corpus questions without traversing every individual document at query time.
• →Graph storage: extracted entities, relationships, and community data are stored in a graph database — Neo4j is the most common enterprise choice. The graph index supplements rather than replaces your vector index; both run together in the hybrid retrieval path.

Building the Hybrid RAG Router in Production

The production architecture for a GraphRAG system has three primary retrieval paths and a query classifier that selects between them. The classifier is the most consequential architectural decision — it determines latency, cost, and quality for the entire system.

• →Query classifier: classify each incoming query into three to five complexity buckets — simple factual (vector path), relationship or multi-hop (graph local search), thematic or cross-corpus synthesis (graph global search), and optionally a fourth bucket for queries needing the full agentic pipeline. Train the classifier on real user queries from your domain — a generic classifier trained on public benchmarks will not match your users' actual patterns.
• →Vector path: the fast default. Embed query, retrieve top-k from your vector store, rerank, generate. Target this path for 80% of queries. End-to-end latency target: sub-500ms.
• →Graph local search path: extract entities from the query, traverse the graph from matched entity nodes, assemble context from traversed nodes and their associated chunks, generate. Latency target: 1.5–3 seconds. Use for questions that name specific entities and require following their relationships.
• →Graph global search path: query against pre-computed community summaries rather than raw chunks. Answers broad thematic questions with lower latency than local search because summaries are pre-computed; the tradeoff is lower specificity on entity-anchored queries.
• →Fallback: when graph traversal returns no relevant nodes because entities were not extracted or relationships do not exist in the graph, fall back to vector retrieval automatically. Never leave a query with an empty context.

GraphRAG Tools and Frameworks in 2026

The GraphRAG ecosystem matured significantly in 2025–2026. Teams no longer need to build entity extraction and graph construction pipelines from scratch. The AI & automation engineering work we do for enterprise clients typically starts with one of these production-ready options rather than a custom build:

• →Microsoft GraphRAG (open source): the reference implementation that established the technique in production settings. Handles entity extraction, Leiden community detection, community summarization, and both local and global query modes out of the box. Production-ready for corpora up to tens of millions of tokens; requires cost governance at scale because of high LLM call volume during indexing.
• →LlamaIndex PropertyGraphIndex: integrates directly with LlamaIndex's document loading, chunking, and query pipeline. Best choice if your team already uses LlamaIndex for vector RAG — you add the graph layer without a full architecture change. Supports Neo4j, Amazon Neptune, and in-memory graph backends.
• →LangChain with Neo4j: LangChain's Neo4j integration supports hybrid vector + graph retrieval. GraphCypherQAChain translates natural language queries to Cypher graph queries, handling a significant portion of the graph query translation problem automatically.
• →Neo4j GraphRAG Python library: Neo4j's native library provides entity extraction, graph storage, and hybrid retrieval in a single package optimized for the Neo4j database. Strong choice when Neo4j is already part of your data infrastructure.
• →Amazon Neptune Analytics: fully managed graph database and analytics service on AWS with built-in vector similarity search. The right choice for teams running in AWS who want graph RAG with managed operations and no separate graph database to operate.

GraphRAG Indexing Cost and Latency: What to Budget For

GraphRAG's primary operational cost is the offline indexing phase. Every document chunk requires at least one LLM call for entity and relationship extraction, and community summarization adds another pass. On a 1-million-token corpus using an efficient extraction model, extraction costs approximately $150–$300 in LLM API spend. Using a frontier model for high-accuracy extraction on a 10M-token corpus runs $1,500–$5,000 for a single full reindex. These costs are paid once at initial build and then incrementally as documents are added — not per query.

• →Indexing latency: a full reindex of a 1M-token corpus takes 2–4 hours with parallelized extraction at typical API rate limits. Plan for incremental indexing on document updates rather than full reindexes after the initial build.
• →Query latency: graph local search adds 800ms–2s to baseline vector retrieval latency due to entity extraction from the query and graph traversal. Global search using pre-computed community summaries is faster at 400–800ms overhead. Budget these into your latency SLOs before committing to the architecture.
• →Stale graph data: unlike a vector index where updating one chunk changes one vector, updating a knowledge graph requires re-extracting entities from updated documents and potentially re-running community detection on affected subgraphs. Build incremental update pipelines from day one — retrofitting after launch is expensive.
• →Schema governance: the entity types and relationship types your extraction prompts are designed to find determine what questions the graph can answer. Define your entity taxonomy before you build — a graph optimized for customer-product relationships does not automatically support organizational hierarchy traversal without a schema extension.

Frequently Asked Questions

What is GraphRAG?

GraphRAG (Graph Retrieval-Augmented Generation) is a retrieval technique that builds a knowledge graph of entities and relationships from your document corpus and uses graph traversal alongside vector similarity search to answer questions. It was introduced by Microsoft Research and handles multi-hop, relational, and thematic questions that standard vector RAG cannot answer, because it stores and queries explicit entity relationships rather than relying solely on embedding similarity.

When should you use GraphRAG instead of vector RAG?

Use GraphRAG when your users regularly ask multi-hop questions that require connecting information across multiple documents through entity relationships — compliance gap analysis, contract review, supply chain reasoning, customer 360 lookups. If your RAG system consistently fails on these question types despite good vector retrieval quality (measured by RAGAS Context Recall), the graph layer is the right fix. If your users primarily ask self-contained factual questions, vector RAG alone is faster, cheaper, and easier to operate.

What is the difference between GraphRAG local search and global search?

GraphRAG local search traverses the knowledge graph from specific entities mentioned in the query, retrieving their connected nodes and associated document chunks. It answers entity-anchored questions like 'What are all the contracts between Company X and Company Y?' Global search uses pre-computed community summaries to answer broad thematic questions like 'What are the main compliance risks in our contracts?' — questions that require synthesizing across the entire corpus rather than drilling into specific entity relationships.

How much does GraphRAG indexing cost?

A rough estimate: $150–$500 per million tokens of source documents using efficient extraction models like Claude Haiku or GPT-4o mini. Frontier model extraction improves relationship quality but costs 5–10x more. Incremental updates to add new documents are proportional to the new content added; a full reindex is typically only required when your entity schema changes significantly. Query costs are comparable to standard RAG — graph traversal itself adds no LLM cost, only graph database read operations.

What graph database should I use for GraphRAG?

For most enterprise teams: Neo4j if you want the largest ecosystem, strongest GraphRAG library support, and mature enterprise features including role-based access and audit logs. Amazon Neptune if you run in AWS and want a fully managed graph database service. TigerGraph for very large-scale graphs with billions of edges and strict latency requirements. pgvector with Apache AGE if you already run PostgreSQL and want to avoid operating a separate database — this reduces operational overhead at the cost of graph query performance at scale.

How Belsoft Helps With Enterprise RAG Architecture

Belsoft designs and builds production RAG systems that include the graph layer where it earns its keep — not as a pattern to apply everywhere, but as a targeted solution to the specific retrieval failures that vector search cannot fix. We build hybrid retrieval architectures with query routing, GraphRAG pipelines for contract analysis and compliance use cases, and RAG evaluation frameworks that measure multi-hop accuracy alongside standard RAGAS metrics. If your enterprise AI system is failing on the questions that matter most, or you are evaluating whether GraphRAG is the right investment for your use case, our AI & Automation engineering team can run a retrieval audit against your actual query distribution and build a proof-of-concept before you commit to the infrastructure.

GraphRAG is the right answer to a specific class of retrieval failure — not a universal upgrade to apply everywhere. The teams that get the most value are those who identify that failure class through eval data first, then build the graph layer to address it while preserving fast vector retrieval for the majority of queries. If that matches your current situation, book a technical strategy call and we can walk through your retrieval pipeline together.

“Vector search finds what looks similar. Graph search finds what is connected. The highest-value enterprise questions require both.”