pgvector adds vector storage and similarity search to PostgreSQL. It lets an application keep embeddings alongside relational data and query nearby vectors with SQL. PostgreSQL may be enough when it meets your measured requirements for relevance, recall, latency, filtering, throughput, and operational cost; there is no universal row-count cutoff that says when you must move to a separate vector database.
What pgvector adds to PostgreSQL
pgvector is a PostgreSQL extension that provides vector data types, distance operators, and indexes for nearest-neighbor search. Your application continues to use PostgreSQL tables and SQL; pgvector does not replace the database or its relational query capabilities.
A typical query orders eligible records by a vector distance and limits the results. PostgreSQL can also apply ordinary SQL filters, use conventional indexes on filter columns, and combine vector search with its full-text search. Hybrid ranking—for example, combining text and vector rankings with Reciprocal Rank Fusion or a cross-encoder—requires an explicit approach in application logic; it is not an automatic promise of better relevance.
Choose between exact and approximate search
Exact search
Exact nearest-neighbor search is pgvector’s default. The project documentation says it “provides perfect recall”: it finds the nearest stored vectors according to the chosen distance calculation. Exactness does not establish that the embeddings capture what people consider relevant for your application. Without an approximate index, PostgreSQL can rank all rows eligible for the query and return the closest ones.
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HNSW
HNSW is a multilayer graph index. The pgvector project describes it as offering a better speed–recall tradeoff than IVFFlat, at the cost of slower index builds and higher memory use. It can be created before loading data because it has no training step. Its documented default search breadth, hnsw.ef_search, is 40; changing search effort can change the speed–recall tradeoff.
IVFFlat
IVFFlat groups vectors into lists and searches selected nearby lists. It generally builds faster and uses less memory than HNSW, but has a lower speed–recall tradeoff. It needs data to train its lists, so create the index after data is loaded. The documented default number of probes is 1; searching more lists can improve recall while taking longer. Treat the project’s list-count heuristics as starting points, then validate choices on your own data and queries.
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Both HNSW and IVFFlat are approximate: an index can speed up a query while changing which rows it returns. Compare results with exact search and measure recall rather than assuming an index preserves exact results.
Why filters and tenancy can change results
Approximate vector indexes apply metadata filters after scanning the index. That matters for queries such as “nearest items in this category”: the scan may not encounter enough qualifying rows to fill the requested result limit. A conventional index on the filter column may be sufficient for fast exact search when the filter selects a small part of the table.
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The pgvector documentation illustrates the effect with a filter matching 10% of rows and HNSW’s default search breadth of 40: about four qualifying rows would match on average before further scanning. This is an explanatory estimate, not a benchmark or a guarantee for a particular dataset.
Ways to address filtered approximate search
- Iterative scans: Available in pgvector 0.8.0 and later, these continue scanning until enough results are found or a configured limit is reached. Strict ordering preserves exact distance order; relaxed ordering can improve recall while allowing slight reordering.
- Filter indexes: Add ordinary indexes to filter columns when they help the query pattern. Partial indexes can suit a few distinct filter values.
- Partitioning: For many distinct values, partitioning may be appropriate. The project warns that tenants sharing one approximate index can affect one another’s recall and speed; list partitioning or separate tables can provide isolation.
Test filtered queries with realistic tenant distributions and requested result counts. An index that performs well without filters may behave differently when filters discard many scanned candidates.
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How to decide whether PostgreSQL is enough
Do not choose by a generic database-size slogan. Measure the workload you expect to run, including representative vectors, query mix, filters, concurrency, update patterns, and hardware. There is no workload-independent row-count threshold in the pgvector documentation for when PostgreSQL stops being enough.
- Define acceptance targets. Set the task-level relevance and recall you need, plus p50 and p95 latency, throughput, filter behavior, and tenant isolation expectations.
- Build a representative test set. Include real query patterns, metadata filters, expected concurrency, ingestion and update activity, and data at a realistic scale.
- Establish an exact-search baseline. Use exact search as a recall reference, then test HNSW or IVFFlat if query performance requires approximate indexing.
- Measure query plans and resource use. Use
EXPLAIN (ANALYZE, BUFFERS)for query performance. Monitor approximate recall by comparing indexed results with exact search on representative queries. - Evaluate the whole operation. Include index-build time, memory and storage footprint, writes, backups, recovery, and the expertise needed to operate the system—not just query latency.
- Compare alternatives on equal terms. If considering another retrieval system, run the same queries and workload against it. Compare recall and task relevance, p50/p95 latency and throughput, filtering and hybrid search, ingestion and recovery, resource use, operating cost, and operational complexity.
The comparison should answer whether a different system solves a measured shortfall. pgvector’s project documentation supplies no cross-vendor benchmark that can decide this for every application.
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Loading data and operating indexes
For bulk ingestion, the pgvector project recommends loading with COPY and adding indexes after the initial load for better performance. In production, create indexes concurrently when needed to avoid blocking writes. HNSW vacuum work can be lengthy; the project suggests reindexing concurrently before vacuuming.
If storage or index footprint is a constraint, pgvector supports halfvec, a smaller half-precision representation, and binary quantization with reranking. These options introduce representation and recall tradeoffs, so test them against the application’s quality target rather than assuming the reduced footprint is free.
The project’s scaling guidance includes adding memory, CPU, or storage to a single instance, using replicas, and considering sharding tools or approaches. These are options to evaluate against measured needs and team operations, not evidence that a particular provider or separate database is required.
Quick Recap
Practical decision rule
- Stay with PostgreSQL and pgvector if representative tests meet your recall, relevance, latency, filtering, throughput, and operating requirements.
- Tune the approach first if the shortfall is specific to index settings, filtering, memory, or query design; validate any change against exact results and realistic workloads.
- Compare another retrieval system when a measured requirement remains unmet or PostgreSQL’s operational tradeoffs do not suit your application. Use the same workload and evaluation criteria rather than assuming a separate vector database is automatically faster or more accurate.
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