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If your application already uses PostgreSQL, test pgvector against your real workload before adding a dedicated vector database. pgvector stores vectors and searches for similar ones inside PostgreSQL, with exact nearest-neighbor search by default and optional approximate indexes for faster searches. It is a practical starting point—not a guarantee that PostgreSQL will meet every workload’s performance or operational needs.
Do you need a dedicated vector database?
Not necessarily. If PostgreSQL is already part of your application, pgvector lets you keep vector data and similarity queries in the database you already use. That can avoid introducing another service, but it does not prove that pgvector is the best choice for your traffic, latency targets, filtering patterns, or deployment constraints.
The useful question is not how many vectors you have in isolation. It is whether a system can return sufficiently relevant results, within your latency target, under your expected and peak load—and whether you can operate it reliably at an acceptable total cost. The pgvector project documentation does not establish a universal vector-count threshold or a universal performance winner.
What pgvector adds to PostgreSQL
pgvector is a PostgreSQL extension, not a separate database service. It adds vector data types and distance operators to PostgreSQL. The project documentation lists support for PostgreSQL 13 and newer; its documentation reports pgvector 0.8.6, released July 29, 2026. Check the installed extension version and your managed PostgreSQL provider’s supported version before implementing it.
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At a high level, you enable the extension, store embeddings in a vector column, and order results by a distance operator to request nearest neighbors. For example, enable the extension in the database with:
CREATE EXTENSION vector;
That is enough to begin evaluating the approach. Whether you need an approximate index depends on how the unindexed exact search performs for your actual candidate set and response-time requirements.
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Exact search or an approximate index?
By default, pgvector performs exact nearest-neighbor search, which the project documentation says provides perfect recall. In practical terms, exact search does not use an approximate index to skip candidates, so it can return the true nearest results for the query. An approximate index can reduce search time by considering fewer candidates, but may miss some of the true nearest neighbors. That speed-versus-recall trade-off should be measured against your application’s quality requirements.
| Search option | What it means | What to evaluate |
|---|---|---|
| Exact search | Default behavior; searches for the true nearest neighbors. | Latency and resource use for your real query and filtering workload. |
| HNSW | Approximate index. The pgvector documentation describes a generally better speed/recall trade-off than IVFFlat, with higher memory use and slower index builds. | Recall and latency at the chosen settings, memory use, build time, and update needs. |
| IVFFlat | Approximate index. The pgvector documentation describes faster builds and lower memory use than HNSW, with lower query performance in its comparison. | Recall and latency at the chosen settings, build time, memory use, and how the index behaves as data changes. |
HNSW exposes settings including m, ef_construction, and hnsw.ef_search. IVFFlat exposes lists and ivfflat.probes. These are tuning controls, not magic defaults: measure the resulting latency and recall using your data and query mix. The pgvector project README also gives starting heuristics for IVFFlat list counts and probes; treat them as starting points, not guaranteed optimal values. Build an IVFFlat index after the table has data.
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Why filters can change the result
Approximate-index filtering needs particular attention. With these indexes, filtering is applied after the index scan. If the scan finds too few candidates matching a filter, the query can return fewer results than requested—even when more matching rows exist elsewhere in the table.
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. This is an illustrative example from the documentation, not a performance prediction for other workloads. The project documents iterative scans, which can keep scanning until enough rows are found or a configured limit is reached.
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For filtered searches, the project documentation suggests starting with a B-tree index on the filter columns. Other approaches depend on the shape and number of filter values:
- A few common filter values: consider partial indexes tailored to those values.
- Many distinct filter values: consider partitioning.
- Tenant-specific searches: account for how a shared approximate index across tenants can affect recall and speed. The documentation describes list partitioning or separate tables as isolation options.
Test the number of matching results after filters, not only raw nearest-neighbor latency. This matters especially when the application needs a fixed number of results for a tenant or a narrowly filtered category.
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Can hybrid search stay in PostgreSQL?
Yes. The pgvector project documentation shows combining vector search with PostgreSQL full-text search. Keeping both kinds of search in PostgreSQL may fit an application that needs lexical and semantic signals together, but the database does not choose the right ranking for your product. You still need to decide how to combine signals and evaluate whether the resulting order is useful for real queries.
How to decide whether to switch
Run the same representative data and queries through pgvector and any dedicated service you are considering. Compare the whole workload rather than a single headline latency or vector count:
- Latency: measure normal and peak load against the response-time target your application needs.
- Recall or task quality: check whether approximate search returns results good enough for the user’s task at that latency.
- Filters and isolation: test filter selectivity, tenant separation, and how many results remain after filters.
- Index and data operations: account for index build time, memory, updates, and ongoing maintenance.
- Search behavior: evaluate whether lexical and semantic ranking requirements can be met with the design you plan to operate.
- Deployment and cost: include PostgreSQL integration, reliability needs, deployment constraints, and total operating cost.
If pgvector meets your measured requirements and fits your operational setup, there may be no reason to add another database. If it cannot meet a requirement that matters—such as recall at your latency target, filtered-result behavior, reliability, or operational constraints—a dedicated system may be worth evaluating. Make that decision from comparable workload results, not from a presumed scale threshold.
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