pgvector vs Dedicated Vector Database: When Postgres Is Enough

The default reaction to "we need vector search" is to procure a vector database. For many teams the better first answer is the Postgres they already run, with the pgvector extension. This comparison covers when that is enough, when it is not, and how to decide with measurements rather than assumptions. It draws on FISTA Solutions' AI enablement practice and complements how to choose a vector database and pinecone vs weaviate.

What is pgvector?

pgvector is an open-source PostgreSQL extension that adds a vector data type, distance operators, and approximate nearest neighbor indexes. Embeddings live in ordinary tables beside business data, queries combine similarity with SQL filters and joins, and the database's transactions, backups, replication, and access control apply unchanged. Its appeal is that it adds capability to a system the team already operates and trusts.

What is a dedicated vector database?

A dedicated vector database is a system built around vector search: specialized index structures, distributed scaling for very large collections, filtering designed for vector workloads, and features such as native multi-tenancy, built-in hybrid search, or integrated vectorization. Examples include managed services and open-source engines. Its appeal is performance and features at scale, at the cost of another system to operate or govern.

How do they compare?

Dimension	pgvector	Dedicated vector database
Operations	One extension on an existing database	New system or managed service
Scale	Strong at moderate scale; limits depend on hardware and index choice	Designed for very large collections
Filtering	SQL predicates; performance depends on index and selectivity	Purpose-built filtering; behavior varies by product
Hybrid search	Full-text search plus vectors, fused in query or application	Often built in
Consistency with business data	Transactions and joins	Separate system; consistency managed by application
Multi-tenancy	Schemas, row-level security, partitioning	Native features in many products
Freshness	Immediate on commit	Depends on product; usually near real time
Specialized features	Fewer	More: index types, quantization options, rerankers
Cost	Existing infrastructure; scale with the database	Service pricing or dedicated infrastructure

Capabilities in both categories change quickly; verify current details.

When is pgvector the right choice?

Choose pgvector when you already operate Postgres competently, your vector collections are moderate in size, your filters are ordinary SQL predicates, you value transactional consistency between embeddings and business records, and operational simplicity matters. Many enterprise knowledge assistants, product search features, and internal tools fall within this envelope. The retrieval quality of those systems is decided by chunking, embeddings, hybrid retrieval, and reranking, as described in the enterprise RAG reference architecture whitepaper, not by the store.

When is a dedicated store the right choice?

Choose a dedicated store when benchmarks show pgvector cannot meet recall or latency targets at your vector count, when heavy filtering under load degrades performance, when you need native multi-tenancy at large tenant counts, when the vector workload's memory and compute would contend with transactional workloads, or when specialized features materially simplify your architecture. Very large consumer-scale search and recommendation systems commonly land here.

What should you benchmark?

1. Recall against exact search at your target latency, on your embedding dimensionality and count.
2. Latency percentiles at expected concurrency, with and without realistic filters.
3. Index build time and memory for your collection size.
4. Write and delete throughput for your freshness needs.
5. Resource contention with transactional workloads if sharing an instance.
6. Cost at projected scale over a multi-year horizon.

Benchmark method is discussed in how to build a vector search service and cost in vector database cost.

How do you keep the decision reversible?

Put the store behind a vector search service with your own API for upsert, delete, search, and filters, and tenant scoping enforced in the service. Start with pgvector; if a collection outgrows it, reindex that collection into a dedicated store and update the service's routing. Applications never change. This is the pattern FISTA applies so that the first decision does not become a permanent one.

What about hybrid search in Postgres?

Postgres full-text search provides the lexical side of hybrid retrieval; combined with pgvector similarity and fusion logic, teams get hybrid search in one database. It requires assembly that some dedicated stores provide out of the box, but it keeps one operational surface. The hybrid layer design is in how to build a hybrid search system.

What does the decision look like in practice?

An organization building an internal knowledge assistant over a few hundred thousand chunks, with permission filtering by department and an existing managed Postgres, benchmarks pgvector and finds recall and latency within targets; it ships on pgvector behind a vector search service. A year later, a product search feature with tens of millions of vectors, heavy attribute filtering, and strict latency shows pgvector struggling under load; that collection moves to a dedicated store through the same service, while the knowledge assistant stays where it is. Both decisions were made on measurements, and neither required application changes.

How FISTA Solutions decides

FISTA Solutions starts from the client's existing platform and scale, benchmarks pgvector against dedicated candidates on the client's embeddings and filters, and deploys the winner behind a vector search service that keeps the choice reversible. The AI enablement practice delivers the service and retrieval layer, AI agents and product features consume it, and forward deployed engineers run the benchmark with your team. The record behind the approach is 150+ projects with 99.9% uptime.

To benchmark your workload, message FISTA on WhatsApp, or read postgres vs mongodb for ai apps for the broader database decision.