pgvector is the natural fit when you want vector retrieval inside PostgreSQL; OpenSearch is the fit when your application’s search workflow belongs in a search engine. Both support approximate vector search and hybrid retrieval, but their filtering behavior, index options, and operational context differ. Neither is universally faster or more accurate: choose by testing the specific engine, method, filters, and workload you plan to run.
How pgvector and OpenSearch differ at the system level
pgvector is a PostgreSQL extension for storing vectors and searching for similar ones alongside relational data. OpenSearch provides vector search through knn_vector fields in a search-oriented indexing and query environment. That difference should shape the first decision: do you want vector retrieval to share PostgreSQL’s data and SQL workflows, or do you want it in the system where your application already performs search?
| Decision area | pgvector | OpenSearch |
|---|---|---|
| System context | Vector similarity search in PostgreSQL, alongside relational data and SQL. | Vector k-NN search in a search engine, using indexed vector fields and search queries. |
| Exact search | Exact nearest-neighbor search is the default when no approximate index is used; the pgvector README says this provides perfect recall. | Offers exact approaches, including scoring-script search, as well as approximate k-NN. |
| Approximate index options | HNSW and IVFFlat. | HNSW and IVF, implemented through different engines, including Faiss and Lucene. |
| Hybrid retrieval | Can combine PostgreSQL full-text search and vector search; the project documents reciprocal rank fusion and cross-encoder approaches. | Provides hybrid queries with search-pipeline processing, including score normalization and reciprocal rank fusion. |
The table compares documented capabilities, not equivalent configurations or measured performance. In particular, an algorithm name alone does not make two implementations interchangeable.
Exact search, approximate search, and index choices
Start with an exact-search baseline
Exact nearest-neighbor search is useful as a reference for whether an approximate configuration returns the neighbors your application needs. Approximate search reduces search work at the cost of potentially missing some true nearest neighbors. OpenSearch’s vector-search documentation describes approximate search as the best option for most use cases, but that is product guidance, not a guarantee for every workload.
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Measure approximate results against exact results using the same vectors, distance metric, filters, and query set. Report recall alongside latency: a faster result is not a useful improvement if it misses too many relevant neighbors.
pgvector: HNSW or IVFFlat
The pgvector project describes HNSW as offering a better speed–recall trade-off than IVFFlat, with slower index builds and higher memory use. HNSW does not require the training step needed by IVFFlat, so an HNSW index can be created before the table contains data. IVFFlat builds faster and uses less memory, while offering a lower speed–recall trade-off in the project’s qualitative comparison. These are project-level descriptions, not guarantees for a particular dataset or configuration.
For HNSW, hnsw.ef_search controls the search breadth: raising it can improve recall while increasing query work. For IVFFlat, the number of probes affects the search. Tune these settings against the recall and latency your application requires rather than assuming a default is appropriate.
OpenSearch: choose the engine as well as the method
OpenSearch supports HNSW and IVF through engines with different features and optimizations. Its documentation generally points to Faiss for large-scale use cases and describes Lucene as an option for smaller deployments and smart filtering. Those are maintainer recommendations, not universal size thresholds or independent benchmark results.
When evaluating OpenSearch, record the engine and method together—for example, Lucene HNSW or Faiss IVF—and verify that the combination supports the filtering and tuning behavior you require. OpenSearch’s ef_search setting controls how many vectors HNSW examines; higher values can improve recall at the cost of latency. Available parameters depend on the selected engine and method.
How filtered vector search changes the comparison
A vector query often also needs a condition such as tenant, category, status, or date range. The point at which that condition is applied affects both how many results are returned and how representative they are. Test with the real filter distribution and selectivity—not just an unfiltered query.
Filtering with pgvector approximate indexes
For approximate indexes, pgvector applies filters after the index scan. Its README illustrates the consequence with a condition matching 10% of rows: with the default hnsw.ef_search of 40, an average of four qualifying rows would be expected. This is an illustrative calculation in the project documentation, not a measured result for every dataset.
If filtered searches return too few rows, pgvector documents several approaches:
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- Partial indexes: consider these when there are only a few distinct filter values and separate indexes are practical.
- Partitioning: consider it when there are many values and the data can be divided accordingly.
A shared approximate index across tenants can allow one tenant’s vectors to affect another tenant’s recall and speed. The pgvector project suggests partitioning or separate tables when tenant isolation is needed; validate the design using the application’s actual tenant and query patterns.
Filtering with OpenSearch
OpenSearch distinguishes filtering during approximate k-NN search from filtering after that search. Its documentation lists efficient in-search filtering for Lucene HNSW in OpenSearch 2.4 and later, Faiss HNSW in 2.9 and later, and Faiss IVF in 2.10 and later. These version gates describe the combinations in the cited documentation; check the documentation for the version you deploy.
Other query paths have different semantics. A Boolean filter or post_filter can filter after approximate search, which may leave fewer qualifying results. A scoring-script approach can pre-filter and then perform exact search. Compare the result count and recall of the path you intend to use; the presence of filter syntax alone does not establish that filtering happens efficiently within k-NN search.
Hybrid keyword and semantic retrieval
Both systems can combine lexical search with vector similarity, but combining two result sources still requires a ranking strategy.
PostgreSQL with pgvector
The pgvector project documents using PostgreSQL full-text search alongside vector search. Its examples include reciprocal rank fusion, which combines results by rank, and cross-encoder reranking. Choose based on the desired retrieval and ranking pipeline, then measure the quality of the combined results on representative queries.
OpenSearch hybrid queries
OpenSearch hybrid search combines keyword and semantic results through a search pipeline. Its documentation says hybrid search was introduced in OpenSearch 2.11. A normalization processor rescales and combines scores; a score ranker uses reciprocal rank fusion, combining results by rank rather than raw score values. Select and tune the pipeline intentionally, since the choice changes how keyword and semantic results contribute to the final ordering.
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There is no controlled pgvector-versus-OpenSearch benchmark established by the product documentation cited here. Build a matched test around the workload you expect to run, and keep the test configuration explicit enough to reproduce.
Quick Recap
- Use the same data and queries. Match the embedding vectors, query set, distance metric, and relevant filter conditions. Include the selectivity and tenant distribution expected in production.
- Establish exact-search results. Use exact search where feasible to create a reference set for recall. Then compare each approximate configuration against that reference.
- Record full configurations. For pgvector, record HNSW or IVFFlat and settings such as
hnsw.ef_search, probes, and iterative-scan controls. For OpenSearch, record the engine, method, filtering path, and supported search parameters such as HNSWef_search. - Measure quality and speed together. Report recall and p50/p95 query latency, including filtered queries. Measure whether each query returns enough qualifying results, not only how quickly it returns something.
- Include resource and data-change costs. Measure index build time, storage and memory use, and the behavior of the ingestion and update patterns your system needs. Include the operational effort of the surrounding database or search deployment in the decision.
- Repeat under representative conditions. Run the same query mix and filter patterns on the intended deployment class, and test relevant update activity rather than relying on a static, unfiltered dataset.
Which should you choose?
- Favor pgvector for evaluation when keeping vector retrieval in PostgreSQL alongside relational data and SQL fits your application’s architecture. Compare HNSW and IVFFlat against the required recall, build, and memory profile, and pay particular attention to filtered result sufficiency.
- Favor OpenSearch for evaluation when vector retrieval belongs in your search-oriented workflow or you need its documented combination of keyword and semantic querying. Select the exact engine and method, and verify that its filter path and version meet your needs.
- Keep both in contention when neither system context settles the choice. The decisive evidence is your measured recall, latency, resource use, filtered-query behavior, indexing and update costs, and operational fit—not a generic claim that one product wins vector search.
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