The “Vector Database” vs. “Vector-capable Relational Database” debate
Should you use a dedicated vector database (e.g. Pinecone, Milvus, Qdrant) or a vector-capable relational DB (e.g. PostgreSQL with pgvector, MySQL with vector support)? The answer depends on workload, scale, and how much you need ACID, joins, and a single store.
Summary
- Choice between a dedicated vector database (e.g. Pinecone, Milvus, Qdrant) and a vector-capable relational DB (e.g. PostgreSQL with pgvector, MySQL with vector support) depends on workload, scale, and need for ACID, joins, and a single store. See definition: VDB vs. relational.
- Vector-capable RDBMS: one system for transactional data, metadata, and vectors; ACID and joins; simpler ops if you already run Postgres/MySQL; vector search may be less optimized, scaling usually vertical or via DB clustering. Dedicated VDB: built for high-throughput, low-latency ANN, rich index options (HNSW, IVF, disk-based), horizontal scaling; often two systems (RDB + VDB) with sync logic.
- For small–medium scale and strong consistency, RDBMS can be enough; for very large collections, sub-10ms p99, or heavy hybrid workloads, dedicated VDB tends to win. Pipeline: choose store → ingest → query (single or dual system). Practical tip: start with pgvector if you already use Postgres; move to a dedicated VDB when scale or latency demands it.
Vector-capable RDBMS
Vector-capable RDBMS: you get one system for transactional data, metadata, and vectors; ACID and joins across vector and scalar columns; and often simpler ops if you already run Postgres or MySQL. Trade-offs: vector search may be less optimized (fewer index types, less tuning for very large or high-QPS vector workloads) and scaling is usually vertical or via the DB’s own clustering.
Dedicated vector databases
Dedicated vector databases: built for high-throughput, low-latency ANN, rich index options (HNSW, IVF, disk-based), and horizontal scaling. You often keep metadata or user data in a relational DB and use the VDB as the vector engine—so two systems and eventual consistency or sync logic.
Practical takeaway
Practical takeaway: for small to medium scale, strong consistency with relational data, and simplicity, a vector-capable RDBMS can be enough. For very large vector collections, sub-10ms p99, or heavy hybrid workloads where the VDB is the primary query path, a dedicated VDB (or a managed one) tends to win. The line is blurring as RDBMSs add better indexes and VDBs add more metadata and SQL-like features.
Pipeline: choose store → ingest → query (single or dual system). Practical tip: start with pgvector if you already use Postgres; move to a dedicated VDB when scale or latency demands it.
Frequently Asked Questions
Dedicated VDB vs. vector-capable relational DB?
Choice depends on workload, scale, and need for ACID, joins, and a single store. Vector-capable RDBMS (e.g. pgvector): one system for transactional data, metadata, and vectors; ACID and joins; simpler ops. Dedicated VDB: built for high-throughput, low-latency ANN, rich index options, horizontal scaling. See definition: VDB vs. relational.
When is a vector-capable RDBMS enough?
For small to medium scale, strong consistency with relational data, and simplicity. Trade-offs: vector search may be less optimized (fewer index types, less tuning for very large or high-QPS vector workloads); scaling is usually vertical or via the DB’s own clustering. See open-source vs. proprietary and scaling.
When is a dedicated VDB better?
For very large vector collections, sub-10ms p99, or heavy hybrid workloads where the VDB is the primary query path. Rich index options (HNSW, IVF, disk-based); horizontal scaling. You often keep metadata in a relational DB and use the VDB as the vector engine—two systems and sync logic. See latency and QPS.
Is the line between them blurring?
Yes. RDBMSs are adding better vector indexes; VDBs are adding more metadata and SQL-like features. See definition: vector database vs. relational database, open-source vs. proprietary, and federated search.