GraphRAG vs. Flat Vector Retrieval: A Production Analysis
After running both systems in parallel on real workloads, the results challenge some widely-held assumptions about when graphs actually win.
A 60-day parallel deployment of GraphRAG and flat vector retrieval systems across three production query domains reveals that neither approach is universally superior. The analysis presents concrete performance data, identifies the query patterns where each architecture wins, and argues for a classifier-routed hybrid as the production baseline.
The marketing pitch for graph-based retrieval is compelling: richer context, multi-hop reasoning, entity relationships that flat vectors cannot capture. But engineering decisions should not be made on marketing pitches. This analysis documents what actually happened when we ran both systems under production-realistic conditions.
Experimental Setup
We ran a 60-day parallel deployment across three query domains:
- Technical documentation: 12,000 source chunks, dense cross-referencing
- Project history: 8 months of meeting notes, decisions, and artifact links
- Code repository metadata: function signatures, module dependencies, changelog entries
Both systems used the same embedding model (text-embedding-3-large) and identical chunking strategies. The GraphRAG layer added entity extraction (Claude Haiku) and relationship indexing into a Neo4j instance. The flat system used Pinecone with cosine similarity retrieval.
Where Flat Vector Retrieval Wins
For single-document lookup — "find me the section about retry logic" — flat retrieval was faster, cheaper, and equally accurate. The overhead of graph traversal adds 40–120ms of latency per query with zero precision gain.
Flat retrieval also wins on corpus volatility. When documents change frequently, maintaining entity consistency across a graph introduces synchronization debt that can poison downstream queries silently.
Where GraphRAG Wins
Multi-hop queries are where the graph earns its existence. Questions like "what decisions led to the current caching architecture?" require traversing a chain of entities (decision → rationale → architecture component → implementation artifact) that flat vectors simply cannot reconstruct.
In our technical documentation domain, GraphRAG improved answer completeness by 34% on multi-hop queries as rated by blind human evaluation. This number was consistent across the 60-day window.
The Hybrid Conclusion
Neither system is universally superior. The correct architecture uses flat vector retrieval as the default path and routes to graph traversal when the query planner detects relational intent signals (temporal chains, causal language, entity co-reference). This routing adds complexity but eliminates the false dichotomy.
Our current production system implements exactly this: a classifier that routes ~70% of queries to flat retrieval and ~30% to graph traversal, with the split dynamically adjusted by query confidence scores.