If your team already runs PostgreSQL, test pgvector against your real workload before adding a dedicated vector database. pgvector is a PostgreSQL extension that stores vectors and supports similarity search in the database you already use. It performs exact nearest-neighbor search by default, with HNSW and IVFFlat indexes available when approximate search may improve speed. That makes it a practical starting point—not a promise that it will outperform every alternative.
Do I need a dedicated vector database?
Not necessarily. If PostgreSQL is already part of your stack, pgvector lets you keep vector data and similarity queries there rather than introducing a separate database service. The project documentation supports PostgreSQL 13 and newer; check both your installed extension version and your managed provider’s supported version before planning deployment.
The useful question is not whether a vector database is inherently better. It is whether your current database can meet your application’s latency, result-quality, filtering, reliability, and operational requirements at the load you expect. There is no universal vector-count threshold in the available documentation that determines when you must switch.
What pgvector gives you inside PostgreSQL
Vector storage and nearest-neighbor search
pgvector adds vector types and distance operators to PostgreSQL. You enable it in a database with:
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CREATE EXTENSION vector;Your application can store embeddings in a vector column and order query results by the chosen distance operator. By default, pgvector uses exact nearest-neighbor search, which the project documentation says provides perfect recall. Exact search can be a good fit when filters leave a relatively small candidate set.
Approximate indexes when exact search is too slow
For workloads where exact search does not meet the latency target, pgvector offers HNSW and IVFFlat indexes. Both make search approximate: they can return results faster, but may miss some of the nearest neighbors. Their trade-offs differ:
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| Search approach | What it trades | Useful consideration |
|---|---|---|
| Exact search (default) | Searches exactly rather than using an approximate index; may not meet latency targets for every workload. | Consider it when the filtered candidate set is small or perfect recall is important. |
| HNSW | Generally offers a better speed/recall trade-off, but uses more memory and takes longer to build. | Test its build and query settings against the recall and latency your application needs. |
| IVFFlat | Builds faster and uses less memory than HNSW, but has lower query performance in the project’s documented comparison. | Build it after the table has data; treat suggested list and probe settings as starting points, not guaranteed optima. |
HNSW settings documented by the project include m, ef_construction, and hnsw.ef_search; IVFFlat settings include lists and ivfflat.probes. The right values depend on your data and query mix, so compare measured recall and latency rather than choosing settings by a rule of thumb alone.
Will filtering and tenant isolation work the way I expect?
Filtering is a key test for approximate search in pgvector. With approximate indexes, filters are applied after the index scan. A scan can therefore find too few qualifying rows even if the table contains enough matches.
The pgvector documentation illustrates the effect this way: if a filter matches 10% of rows, an HNSW query using the default hnsw.ef_search value of 40 returns about four matching rows on average. That is an example of the documented behavior, not a performance guarantee for a different dataset or query.
Ways to handle selective filters
- Try iterative scans. pgvector can continue scanning until it finds enough results or reaches a configured limit.
- Index filter columns. The documentation suggests starting with a PostgreSQL B-tree index on the filter columns.
- Use partial indexes for a few common filter values. If there are many values, consider partitioning instead.
For multi-tenant systems, a shared approximate index across tenants can affect recall and speed. The project documentation describes list partitioning or separate tables as isolation options to evaluate. Benchmark the actual tenant and filter patterns your application serves; the mere presence of a filter in a query does not establish that results will be complete.
Can I do hybrid search in PostgreSQL?
Yes. The pgvector project documentation shows combining vector search with PostgreSQL full-text search. Keeping both forms of retrieval in PostgreSQL can be useful if your application needs lexical matching as well as semantic similarity. You still need to design and evaluate how the two result sets are ranked; having both capabilities in one database does not determine the right ranking for your product.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I compare pgvector with a dedicated service?
Run the same representative data and queries against each option. Include the application’s real filters and expected result counts rather than benchmarking only unfiltered nearest-neighbor queries. Compare:
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- Recall or task quality at the latency target you need.
- Filter selectivity, tenant isolation, and how many results remain after filtering.
- Index build time, memory use, update behavior, and maintenance work.
- Requirements for combining lexical and semantic ranking.
- Fit with your PostgreSQL integrations, deployment constraints, reliability needs, and total operating cost.
Use the measurements to identify the actual constraint. A faster query is not an improvement if result quality falls below what the application can accept; similarly, an index that meets query targets may still be a poor fit if its build, memory, update, or maintenance costs do not work for your deployment.
When should I switch from pgvector to a vector database?
Consider a dedicated service if a representative pgvector setup cannot meet your measured requirements, or if another system’s capabilities and operating model better fit your application. Before switching, make sure you have evaluated the index choices, relevant settings, filtering behavior, and operational costs—not just a single default query.
Make the decision on end-to-end workload results. The documentation establishes pgvector’s search mechanisms and some trade-offs, but it does not establish that pgvector is always faster, cheaper, or simpler than dedicated alternatives, nor does it supply a universal scale limit.
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