GraphRAG vs vector RAG: when to use each

Over the past two years, retrieval-augmented generation (RAG) has become an essential tool for those building applications with large language models (LLMs). However, its main limitation is well known: by splitting documents into chunks and retrieving only those most similar to the question, it fails when the answer requires connecting scattered information or understanding the full picture. For example, questions like 'What are the recurring themes in two years of customer complaints?' go unanswered because no single chunk contains the answer.

The trendy solution is GraphRAG, introduced by Microsoft Research. Instead of feeding the model isolated chunks, it first builds a knowledge graph with the entities and relationships of the corpus, and then uses that structure as context. Although the idea is appealing, does it really improve answers? To answer this, we analyzed Microsoft's original study and four independent evaluations published on arXiv.

What has happened?

The rise of GraphRAG began with Microsoft's paper (arxiv.org/abs/2404.16130), which proposed a method to build knowledge graphs from documents and use them to answer global questions. Since then, variants have emerged such as HippoRAG, which combines graphs with personalized PageRank to improve retrieval. However, adoption has outpaced evidence: many companies implement GraphRAG without knowing whether it truly outperforms vector RAG in their use case.

Why does it matter?

The choice between GraphRAG and vector RAG has direct implications for cost, latency, and answer quality. Vector RAG is simple, fast, and cost-effective, but it has three structural blind spots: it does not connect scattered information, it is blind to global questions, and it loses context at chunk boundaries. GraphRAG addresses these issues with a more sophisticated index that extracts entities, relationships, and communities (using the Leiden algorithm), and generates community summaries used during querying.

The evidence: four studies, one pattern

1. Global and sense-making questions: the big win

The studies agree that GraphRAG shines on questions that require a holistic view of the corpus, such as 'What are the main themes?' or 'What patterns repeat?'. In these cases, vector RAG fails because it only retrieves superficially similar chunks. GraphRAG, by using community summaries, achieves more complete and accurate answers.

2. Local and specific questions: tie or slight edge for vector RAG

When the question is concrete and the answer is in a single chunk, such as 'What was our refund policy in the third quarter?', vector RAG is as good as GraphRAG, but at a fraction of the cost and latency. GraphRAG adds unnecessary complexity for these cases.

3. Cost and complexity: the price of sophistication

GraphRAG requires a much more expensive indexing process, as it involves multiple LLM calls to extract entities and relationships. Additionally, querying involves 'map' and 'reduce' steps that increase latency. The studies show that for most enterprise applications, vector RAG is sufficient and more cost-effective.

What consequences will it have?

The main consequence is that companies must carefully evaluate their use cases before adopting GraphRAG. It is not a silver bullet. For applications that handle large volumes of unstructured data and need to answer global questions (e.g., complaint analysis, market research, financial reports), GraphRAG can be a competitive advantage. But for customer support chatbots or internal search, vector RAG remains the most efficient option.

What should readers know?

If you are considering GraphRAG, keep these points in mind:

• Not everything needs GraphRAG: If your questions are mainly local, stick with vector RAG.
• Measure the cost: GraphRAG can multiply indexing and query costs by 10 or more.
• Evaluate with your own data: Benchmarks are useful, but every corpus is different. Test both approaches with your data.
• Consider hybrids: Some implementations combine both, using vector RAG for local questions and GraphRAG for global ones.

GraphRAG is not a silver bullet; it is another tool in the RAG arsenal. The key is knowing when to use it.

In summary, GraphRAG outperforms vector RAG on global and information-connecting questions, but at a higher cost. The decision should be based on the type of questions you need to answer and your budget. Current evidence suggests that most applications do not need GraphRAG, but for those that do, it can make a significant difference.