pgvector vs Neo4j: Which Do You Actually Need?
One is a Postgres extension for vector search. The other is a purpose-built graph database. They solve different problems — the question is which one is actually yours.
This comparison shows up in searches more often than the products' actual overlap would suggest, because the real decision underneath it usually isn't “pgvector or Neo4j” — it's “does my data need to be searched by similarity, traversed by relationship, or both?” pgvector answers the first question without asking you to run a second database. Neo4j answers the second, and answers it better than almost anything else.
Neither is wrong for what it's built for. The mistake is picking one and later discovering your actual workload needed the other — or both at once.
Side by side
| Criterion | pgvector | Neo4j |
|---|---|---|
| What it actually is | A Postgres extension. Your data stays in regular Postgres tables; vector columns and index types are added on top. | A standalone graph database with its own storage engine, built around a property-graph model from the ground up. |
| Query language | Plain SQL, with vector distance operators (<->, <#>, <=>) usable in a normal WHERE/ORDER BY clause. | Cypher — a pattern-matching language purpose-built for describing relationships and multi-hop paths. |
| Best-fit data shape | Data that is naturally tabular/relational, with vector similarity as one property you search by — product catalogs, document chunks, user embeddings alongside normal rows. | Data that is naturally a network — social graphs, fraud rings, org charts, recommendation-via-connections, dependency graphs. |
| Multi-hop traversal | Possible via recursive CTEs, but this is not what the extension is built for — performance and ergonomics degrade as hop count grows. | The core reason the engine exists. Variable-depth traversal is a first-class, optimized operation. |
| Setup if you already run Postgres | `CREATE EXTENSION vector;` — minutes, no new system to operate. | A new database to install, operate, back up, and monitor alongside Postgres. |
| Vector index support | IVFFlat and HNSW index types, tunable recall/speed trade-offs, native to the extension. | Neo4j added a native approximate vector index in recent versions — usable, but graph traversal is still the engine’s core strength, not vector search. |
| Ecosystem maturity | Inherits all of Postgres — decades of tooling, ORMs, hosting providers, and operational knowledge. | The most mature dedicated graph ecosystem: Cypher, Graph Data Science library, Bloom visualization, and the largest graph-specific community. |
Pick pgvector when…
- You already run Postgres and don't want a second system to operate.
- Your vector search is one feature inside an otherwise relational app — product search, RAG document retrieval, recommendation by embedding similarity.
- You don't need real multi-hop relationship queries; a foreign key or two covers your actual relationships.
Pick Neo4j when…
- Your core queries are shaped like “find everything connected to X within N hops” — fraud rings, dependency chains, social graphs.
- You need a query language built for expressing relationships, not one bolted onto rows-and-columns thinking.
- Vector search, if you need it at all, is secondary to the graph problem you actually have.
Exact query latency depends heavily on your data volume, index configuration, and query shape — benchmark against your own workload rather than trusting any vendor's numbers, including a vendor's comparison page like this one.
If your real answer to the question above is “both” — you need vector similarity and multi-hop graph traversal over the same data, which is exactly what GraphRAG-style retrieval usually needs — running pgvector and Neo4j side by side means syncing two systems and reconciling two query languages. SynapCores runs vector search (HNSW) and Cypher-style graph traversal natively in one engine, over the same tables, in the same query.
See how SynapCores unifies both