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A vector database is useful when an application needs to find items by semantic similarity. If users search for exact IDs, known terms, structured records, or a small body of information that fits in the model’s prompt, an existing database, full-text search, or direct context may be enough. Start with the retrieval task and test the simplest approach that can meet your quality and performance needs.
What problem does a vector database solve?
Vector search represents content as numerical embeddings and retrieves items that are similar to a query in that vector space. It can help when users phrase a question differently from the words used in the source material, or when the application needs similarity-based recommendations or retrieval.
That capability is not automatically needed just because an application uses an AI model. The first question is whether semantic similarity is needed at all, rather than exact matching, structured queries, lexical search, or supplying a small amount of context directly to the model.
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When simpler retrieval is likely enough
Exact identifiers and structured data
For searches by a known customer ID, order number, product code, or other exact key, use a database lookup. For filters and questions about structured fields, use the database’s ordinary query capabilities. Semantic search can add complexity without improving these tasks.
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Known words and phrases
If users search for terms that appear in documents, begin with full-text search and check it against real user queries. Lexical search is a natural fit when matching words and phrases is the intended behavior. A practitioner overview also describes full-text search as a possible alternative to a vector index for some knowledge bases: AppSavvy’s overview, published April 17, 2026.
Small, stable collections
When a knowledge base is small and changes infrequently, test whether it can be included directly in the model’s context. This avoids building a retrieval index, but evaluate prompt size and cost as part of the implementation; direct context is only practical when the material fits the application’s constraints.
Does semantic search require a separate vector database?
No. If PostgreSQL is already part of the stack, pgvector adds vector similarity search there, so a separate vector service is not the only option. The project documentation describes exact nearest-neighbor search as the default and optional HNSW and IVFFlat indexes for approximate search: pgvector project documentation.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteThis makes pgvector a reasonable candidate to evaluate when semantic retrieval is needed and PostgreSQL is already in use. Whether it is sufficient depends on measured relevance, latency, throughput, filtering, update patterns, resource use, and operational requirements—not on a universal corpus-size cutoff.
What to know about pgvector’s search options
Exact search
The pgvector project documentation states: “By default, pgvector performs exact nearest neighbor search, which provides perfect recall.” Exact search does not use an approximate index, so it can be a useful baseline for evaluating whether an indexed approach preserves acceptable results.
Approximate indexes
HNSW and IVFFlat can trade some recall for faster search. The project documentation describes HNSW as offering a better speed/recall tradeoff than IVFFlat, while requiring more memory and taking longer to build. Those are design trade-offs, not a guarantee that one index will perform better for every application. Test both against your own data and workload if they are candidates.
Filtering, hybrid search, and multiple tenants need their own tests
Filtered queries
With approximate indexes, filtering is applied after the index scan, which can leave fewer matching rows than requested. The pgvector documentation describes iterative index scans and also points to partial indexing or partitioning for some filter patterns. Test the actual filters and selectivity your application uses, including whether the result count remains adequate.
Hybrid retrieval
Some applications benefit from combining semantic and lexical retrieval rather than choosing only one. The pgvector project documents using PostgreSQL full-text search together with pgvector for hybrid search. Measure whether the combination improves representative queries against a relevance rubric suited to your product; the existence of a hybrid option does not establish that it will improve every result set.
Multitenant data
A shared approximate index can affect search speed and recall across tenants. The pgvector documentation describes list partitioning or separate tables as possible isolation approaches. These are database design options, not blanket security guarantees: validate tenant isolation in the complete application and database design.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to decide whether to add a separate vector service
- Define the queries. Collect representative user searches and classify them as exact lookup, structured filtering, lexical search, semantic retrieval, or a combination.
- Establish a baseline. Test the simplest plausible approach—database queries, full-text search, or direct prompt context where appropriate—against the real queries.
- Evaluate semantic need. If users need matches by meaning rather than wording, compare vector retrieval with the baseline using a consistent relevance rubric.
- Test PostgreSQL if it is already in your stack. Evaluate pgvector’s exact search first, then compare approximate indexes if speed is insufficient and the recall trade-off is acceptable.
- Test the production-shaped workload. Use the same corpus, query set, expected load, filters, update patterns, and relevance checks when comparing approaches.
- Consider a separate service only against requirements. Compare whether it improves measured quality, latency, throughput, filtering, or operations enough to justify another system to run and pay for.
What to measure before choosing
- Relevance and recall: Do the results contain the material users need, including after approximate indexing?
- Latency and throughput: Does the system respond quickly enough under expected concurrent load?
- Filtering: Do common filters return enough relevant matches, including for selective filters?
- Updates and index builds: How do data changes and index creation affect the application’s operating requirements?
- Resources and cost: Compare memory, compute, and total infrastructure costs rather than considering query speed alone.
- Data locality and consistency: Does the design keep data where the application needs it and meet its consistency expectations?
- Tenant isolation and operations: Can the design enforce the required tenant boundaries, and what additional operational work does it introduce?
The cited pgvector documentation does not prescribe a universal number of vectors at which a separate database becomes necessary, and the available sources do not establish an apples-to-apples performance crossover. Make the decision from the workload you measure rather than a generic size threshold.
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