COMPARISON MATRIX
Vector Databases vs. Graph Databases
Comparing physical database indexing, storage engines, and retrieval semantics for AI context.
Option A
Vector Databases
Option B
Graph Databases
| Capability / Dimension | Vector Databases | Graph Databases |
|---|---|---|
| Core Index Type | HNSW, IVF, DiskANN vector indexes | Adjacency lists, pointer chasing, graph indexes |
| Primary Data Type | Continuous float32/float16 vector embeddings | Entities, relation edges, node attributes |
| Query Latency | Sub-10ms ANN similarity searches | Depends on traversal depth (1-50ms) |
| Schema Enforcement | Schemaless vector payload metadata | Strict or property-graph schemas |
| Scalability | High horizontal scaling (Pinecone, Qdrant, Milvus) | Scale-up or distributed graph partitioning (Neo4j, Memgraph) |