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.
Tomorrow's AI is being built on LanceDB today
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 | Cyper 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 traversa 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.