Traditional Hybrid Databases

Most "hybrid" vector-graph systems bolt two separate databases together. A vector search finds similar names, then you jump to a completely different graph database by reading JSON metadata. Watch the clunky handoff as we try to find great-grandfathers of people named "John."

The Two-Graph Problem

Traditional hybrids maintain two entirely separate graph structures: an HNSW graph for vector similarity and an RDF/property graph for relationships. They're connected only through JSON documents attached to vector entries. Each hop between systems requires serialization, deserialization, and a separate query.

Query Pipeline

Cost Breakdown

Why This Hurts

For our great-grandfather query, the hybrid approach requires: 1 vector search + JSON parse per result + 3 graph hops per result + context switches between engines. Each "hop" crosses a system boundary. The vector database knows nothing about the graph, and the graph knows nothing about similarity. Every connection is manual glue code.