Quick Facts
- GraphRAG achieves up to 56.2% accuracy versus 16.7% for standard LLMs on enterprise queries, with FalkorDB’s 2025 SDK pushing results above 90%.
- Indexing the same corpus costs $50-$200 through GraphRAG versus under $5 with vector embeddings, a gap of up to 40x.
- On single-hop factual lookups, standard vector RAG still edges ahead, with F1 scores of 64.8 versus 63.0 for graph methods in a Michigan State and Meta study.
Two years into the retrieval-augmented generation boom, a clear split has emerged. Vector RAG handles simple, document-level questions well. GraphRAG handles complex, relationship-heavy queries that vector stores cannot. The problem is knowing which one you need before you pay to build it.
The core failure of vector RAG is structural. Chop documents into chunks, embed them, retrieve the closest matches, and hand them to a model. That pipeline breaks the moment a question requires connecting information across multiple documents. A query like ‘What are the recurring themes across two years of customer complaints?’ has no single chunk that contains the answer.
How GraphRAG Works
Microsoft’s GraphRAG system runs a language model over an entire corpus to extract entities and relationships, then builds a knowledge graph. It applies the Leiden community detection algorithm to cluster related entities and pre-generates summaries at multiple hierarchy levels. Queries route to the appropriate level: local queries hit specific entity neighborhoods, global queries aggregate across community summaries.
That architecture pays off in benchmarks. Diffbot’s KG-LM Benchmark found a standard LLM without knowledge graph grounding hit 16.7% accuracy on enterprise queries. The same LLM grounded in a knowledge graph hit 56.2%. On schema-bound queries like KPI tracking and forecasting, vector search scored 0%. GraphRAG continued to perform.
A Lettria hybrid study from December 2024 tested four domains including finance, healthcare, industry, and law. GraphRAG returned 80% correct answers versus 50.83% for vector RAG. In the industry domain, the gap widened to 90.63% versus 46.88%. Microsoft’s own hierarchical community approach reached 86% accuracy compared with 32% for baseline RAG on enterprise benchmarks.
Where Vector RAG Still Wins
A 2025 study from Michigan State University and Meta ran both architectures under identical conditions, using the same chunking, embeddings, and generation steps. On single-hop, factual lookups, plain RAG came out ahead with an F1 score of 64.8 versus 63.0 for the best graph method. On multi-hop reasoning tasks, graph-guided retrieval pulled in front with 70.3% overall accuracy versus 67.0%.
The research published ahead of ICLR 2026 under the name GraphRAG-Bench echoed the same finding: GraphRAG frequently underperforms standard RAG on many real-world tasks. The benchmark was built specifically to identify which question types justify the graph approach.
The Cost Problem
The accuracy gains come with a steep price. The same corpus that costs under $5 to embed into a vector database runs $50 to $200 through GraphRAG’s entity extraction and community summarization pipeline. Microsoft’s original GraphRAG release in 2024 drew wide attention, then lost momentum when teams discovered indexing large datasets could cost $33,000.
Latency compounds the tradeoff. Vector-only retrieval runs at roughly 50 to 100 milliseconds. Graph-enhanced retrieval takes 200 to 500 milliseconds depending on hop depth. Global mode, which uses a map-reduce pattern across all community summaries, can take 8 to 15 seconds. That rules it out for real-time chatbot applications without aggressive caching.
A Practical Decision Rule
The published research points to a straightforward split for engineering teams. Use vector RAG with a reranker for single-hop, document-centric questions. Use GraphRAG for multi-hop questions, relationship traversal, or global sensemaking across a large corpus. For mixed query types in regulated industries, a hybrid GraphRAG on a governed metadata foundation is the recommended path.
The biggest accuracy gap between the two approaches appears on temporal and reasoning-intensive questions, where GraphRAG can gain more than 20 percentage points. Enterprises running supply chain analysis, financial compliance, or fraud detection, where data is highly interconnected, are the most likely candidates to justify the indexing cost.
For teams already running vector RAG pipelines that work well on factual queries, the evidence argues against switching. For teams hitting a wall on complex, multi-part questions, the benchmarks suggest GraphRAG is worth the investment.
Read more: Stop graphing everything: When GraphRAG actually beats vector RAG