GraphRAG alternative: Skip the graph database complexity.
GraphRAG doesn't require Neo4j or Memgraph. LanceDB handles vector retrieval with native full-text and hybrid search — add graph structure in your application layer if you need it.
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Vector-first beats graph-first for retrieval
Neo4j is powerful for true graph workloads. But if you're doing GraphRAG, you need fast vector retrieval with optional relationship traversal — not a graph database with vector features bolted on. LanceDB gives you hybrid search natively.
Neo4j
LanceDB
Architecture
Graph-first with vector features
Vector-first with native hybrid search.
Complexity
Cypher queries, graph modeling
SQL-like queries, columnar storage.
Search
Vector via plugin. No native hybrid.
Native vector + full-text + SQL hybrid in one query.
Cost
Graph database pricing.
Object storage. Up to 100x savings.
Scale
Graph traversal limits
20 PB largest table. 20K+ QPS.
Best for
True graph analytics, complex traversals
GraphRAG retrieval, vector-first workflows.
GraphRAG doesn't need a real-time graph engine
Memgraph is built for real-time graph analytics. That's overkill for most GraphRAG use cases where you need vector search with native full-text and hybrid retrieval.
Memgraph
LanceDB
Architecture
In-memory graph engine
Disk-native vector database with compute-storage separation.
Cost
Memory-bound pricing
Object storage rates. Up to 100x savings.
Search
Graph queries. Vector via extension.
Native vector + full-text + SQL hybrid in one query.
Complexity
Graph modeling required.
Simple columnar tables with schema evolution.
Best for
Real-time graph analytics.
Vector retrieval with native hybrid search.
Talk to Engineering
Or try LanceDB OSS — same code, scales to Cloud.
Schedule a Technical Consultation