Graph RAG vs Vector RAG Benchmark - Speed and Accuracy Tradeoffs

ShopMax India's AI team is evaluating whether to use Graph RAG or Vector RAG for their product assistant. Vector RAG retrieves semantically similar text chunks and works well for open-ended questions about product descriptions. Graph RAG traverses structured relationships and excels at multi-hop questions like 'What accessories fit the TV that Rahul bought?' Both have different latency and accuracy tradeoffs depending on question type. Running a side-by-side benchmark on the same ShopMax India dataset gives the team concrete data to decide which approach to use - or whether to combine both.

The benchmark tests two question categories. Semantic questions ('What are the features of the Samsung QLED?') favor Vector RAG because the answer is in a product description paragraph - dense retrieval finds it fast. Relational questions ('Which customers bought a TV but no accessory?') favor Graph RAG because the answer requires joining Customer, Order, and Product nodes - vector similarity cannot navigate graph structure. The benchmark records latency in milliseconds and answer quality as a binary correct/incorrect against a ground truth answer for each question.

The example below sets up a FAISS vector index on ShopMax India product descriptions and a Neo4j graph with order data, then runs four questions through both systems and prints a comparison table of latency and correctness.

It gives the following output,

Question                                      VecRAG(ms)   GraphRAG(ms)
-----------------------------------------------------------------------
What is the refresh rate of the Samsung QLE... 423ms        1850ms
What is the battery capacity of the OnePlus... 389ms        1920ms
Which customers placed more than one order?    1340ms       780ms
What is the total revenue from TV purchases?   1200ms       610ms

The benchmark confirms the expected pattern: Vector RAG wins on simple semantic questions (400ms vs 1900ms) because it skips graph traversal. Graph RAG wins on relational aggregation questions (600ms vs 1300ms) because it runs native Cypher instead of asking the LLM to synthesize from text chunks. For ShopMax India, the right architecture is a router: classify the incoming question as semantic or relational, then dispatch to Vector RAG or Graph RAG accordingly. LangChain's RouterChain or a simple zero-shot classification call can act as the router. This hybrid approach gets the best of both worlds without the tradeoffs of either alone.