There is no universal winner. If your application already depends on PostgreSQL and needs vector results alongside relational data, start with pgvector and measure it against your real workload. Consider a dedicated vector service when a specific constraint—such as Postgres resource contention, filtered-search behavior, or operating preferences—justifies a separate system. The decision should come from tests of your data, filters, update pattern, recall target, and latency needs, not from a product label.
What is the difference?
pgvector is a PostgreSQL extension that adds vector data types and similarity search. It keeps embeddings in the same database environment as relational records, where SQL queries can combine vector retrieval with ordinary database operations.
A “dedicated vector database” is not one uniform product category: deployment models and capabilities vary. For a concrete example, Pinecone describes a managed alternative in which customers write to an index while Pinecone operates query servers. Its claims about when that model is advantageous are vendor positioning, not an independent benchmark.
The practical distinction is therefore integrated versus separate operations. With pgvector, vectors share PostgreSQL’s data environment and resources. With a separate service, vector search has its own operating surface, and data may need to be moved or synchronized between systems.
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How pgvector search works
Exact search
Without an approximate index, pgvector performs exact nearest-neighbor search. The project README says this provides perfect recall; as the corpus grows, searching exactly can take longer.
Approximate indexes: HNSW and IVFFlat
Approximate indexes can improve search speed while possibly missing some of the true nearest neighbors. The right choice depends on the balance of query performance, recall, index construction, and memory that your application can accept.
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| Index | How it works | Documented trade-offs | Operational notes |
|---|---|---|---|
| HNSW | Builds a multilayer graph for search. | The pgvector project describes a better query speed/recall trade-off than IVFFlat, but slower index construction and higher memory use. | Does not require training and can be created before table data exists. Search and graph-construction parameters affect recall, query speed, and build or insert speed. |
| IVFFlat | Divides vectors into lists and searches a subset. | The project describes faster index construction and lower memory use than HNSW, with a less favorable query speed/recall trade-off. | Build after data exists. List and probe counts affect speed and recall; generic starting points are not guarantees for a particular dataset. |
These are documented trade-offs, not a universal ranking. Compare both indexes on representative data and tune them to the recall and latency your application requires.
Why filtering can change the result
With approximate indexes, pgvector applies SQL filters after scanning the vector index. That matters when a predicate is selective: the scan may find too few matching rows to fill the requested result count.
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The pgvector README illustrates the effect: at a 10% filter match rate and HNSW’s default search breadth of 40, about four rows match on average. That is an illustrative expectation, not a guarantee for every query. If an application requests k results, test how often filtering leaves it with fewer than k.
The project documents iterative index scans starting with pgvector 0.8.0, which can continue scanning to find more qualifying rows. Depending on the query pattern, partial indexes or partitioning may also help. Measure these approaches with your actual predicates instead of assuming that increasing search breadth or adding partitions will solve every case.
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Tenant boundaries
In a shared approximate index, vectors belonging to other tenants can affect a tenant’s search speed and recall. The project suggests considering list partitioning or separate tables for tenant isolation. The right layout depends on tenant count and query patterns; one partition per tenant is not automatically the best design.
Choose based on your workload
| Question | What to evaluate | What it may indicate |
|---|---|---|
| Do vector results need to join or transact with Postgres records? | How tightly retrieval depends on relational data already in PostgreSQL. | If that integration is important, pgvector is a natural starting point. Pinecone identifies keeping vectors with relational data as a pgvector advantage. |
Do filtered queries need to return a full k? |
Filter selectivity, recall, result counts, and behavior under the actual predicates. | Selective filters on approximate indexes need deliberate testing and may require iterative scans or a different data layout. |
| Can the index fit the memory budget without harming other database work? | Index memory and build requirements, query resources, and competition with the rest of the Postgres workload. | If PostgreSQL resource contention is a real constraint, evaluate whether separating vector search addresses it. |
| How often does the corpus change? | Insert, update, and delete rates, and the index’s behavior under those changes. | Pinecone argues that frequently changing data can favor its managed product. Treat that as the vendor’s position and test your update pattern. |
| Is another system operationally worthwhile? | Deployment, monitoring, availability, security, data movement, and total spend across both systems. | A separate service may suit an operational requirement, but it also creates another system to run or depend on. |
A practical evaluation plan
- Start with the integrated option when it fits. If your application already relies on PostgreSQL and needs vector results alongside relational records, benchmark pgvector using the real query mix.
- Set a quality target. Decide whether exact search meets latency needs. If not, compare HNSW and IVFFlat against an exact-search baseline or another suitable ground truth, with an explicit recall target.
- Include difficult filters. Test representative filter selectivity and tenant boundaries. Record latency, recall, and how often queries return fewer than the requested
k; evaluate iterative scans or data-layout changes where needed. - Compare a named service when a constraint warrants it. Evaluate a dedicated option if Postgres contention, workload growth, filtering needs, or your operational model make a separate system worth considering. Do not infer superior speed or lower cost from the category name.
- Make results reproducible. Record dataset size, vector dimensions, similarity metric, hardware or service configuration, index parameters, filter selectivity, concurrency, recall method, and test date.
So, when should you move from pgvector?
Move only when measurements or operational requirements show that keeping vector search in PostgreSQL no longer fits. That might mean the index’s memory or query load is competing with other database work, your filtered-search requirements are not being met with an acceptable design, or your team prefers a separately managed vector service. Test the candidate service against the same workload and requirements before making the change; there is no neutral, portable benchmark here that establishes one choice as universally faster, cheaper, or more scalable.
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