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You do not need a vector database unless your application needs to retrieve items by semantic similarity. Exact identifiers, structured database queries, full-text search, or a small body of context placed directly in a model prompt may already solve the problem. If semantic retrieval is necessary, an existing PostgreSQL database with pgvector may be enough; a separate vector service is a workload decision, not a default requirement.
First ask what kind of retrieval the application needs
Vector search represents content as numerical embeddings and retrieves items that are close to a query in that vector space. That is useful when people describe the same idea in different words, or when relevant material cannot be found reliably through literal terms alone. But similarity is not automatically better search. Choose the simplest retrieval method that meets the needs of the actual queries.
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- Exact identifiers: Look up an order number, account ID, SKU, or other known key with a database query.
- Structured questions: Use the database’s ordinary filters and queries when users are asking for records that match fields such as date, status, owner, or category.
- Known words and phrases: Start with full-text search and check whether it handles the wording people actually use.
- Small, stable reference material: Try providing the relevant context directly in the model prompt before building an index. AppSavvy describes prompt context and full-text search as alternatives for small or static knowledge bases (AppSavvy, April 17, 2026); this is practitioner guidance, not a universal size cutoff.
- Meaning-based discovery: Consider embeddings when users need to find relevant content despite differences in vocabulary or phrasing.
These approaches can coexist. An application may use exact lookups for account records, full-text search for named terms, and semantic retrieval for open-ended questions.
Semantic search does not automatically require a separate database
If PostgreSQL is already part of your stack, pgvector adds vector similarity search to that database. Its documentation says exact nearest-neighbor search is the default and provides perfect recall. Optional HNSW and IVFFlat indexes can speed up searches by trading away some recall (pgvector project documentation, accessed October 7, 2026).
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That makes PostgreSQL with pgvector a reasonable option to evaluate before adding another service. It keeps vector retrieval within an existing database environment, but whether that is a good fit depends on the workload and your database’s operational requirements. A dedicated vector database is one possible architecture, not a prerequisite for semantic retrieval.
Understand the speed, recall, and resource tradeoffs
Exact nearest-neighbor search checks candidates without the recall tradeoff of an approximate index, though its runtime may become unsuitable for a particular workload. Approximate indexes aim to make retrieval faster by searching less exhaustively, so some relevant neighbors can be missed. The useful choice depends on both response time and the quality of results your application needs.
| Approach | Documented behavior | What to evaluate |
|---|---|---|
| Exact search | Default in pgvector; perfect recall, according to the project documentation. | Latency and resource use with your real data and expected load. |
| HNSW approximate index | Trades recall for speed; pgvector documentation describes a better query speed/recall tradeoff than IVFFlat, with greater memory use and longer build time. | Whether its recall, query performance, memory footprint, and index-build time suit your application. |
| IVFFlat approximate index | Trades recall for speed; documented as requiring less memory and taking less time to build than HNSW. | Whether its result quality and query performance meet your requirements. |
These descriptions are not a substitute for a benchmark: the documentation does not establish an application-independent speed advantage or crossover point. Compare candidate approaches using the same corpus, query set, load, and relevance checks.
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Approximate-index filtering can behave differently from filtering an exhaustive result set. The pgvector project notes that filtering is applied after an approximate index scan, which can leave fewer matching rows than requested. It documents iterative index scans as one response, and points to partial indexes or partitioning for some filtering patterns.
For multitenant workloads, a shared approximate index can affect retrieval speed and recall across tenants. The project describes list partitioning or separate tables as isolation approaches. These are database design options, not blanket guarantees of tenant security: validate access controls and isolation in the application and database design you deploy.
Combine lexical and vector retrieval when either alone falls short
Semantic retrieval can help with meaning-based queries, while full-text search can be useful for exact names and terms. pgvector documents combining vector search with PostgreSQL full-text search for hybrid search. Test the combination against representative queries: hybrid retrieval is worthwhile only if it improves relevance under a rubric suited to your application.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use evidence from your workload to decide whether to add a service
There is no universal vector-count threshold in the cited documentation that says when a separate vector database becomes necessary. Instead, compare the existing design with alternatives against the requirements you actually have:
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11- Relevance and recall: Are the results useful, and how often does approximate search miss items the application should retrieve?
- Latency and throughput: Does the system meet response-time and query-volume needs under expected load?
- Filtering: Do selective filters return enough matching results, including when approximate indexes are used?
- Updates and builds: How do data changes and index construction affect freshness and service behavior?
- Resources: What memory and compute do the current and proposed designs use?
- Data and operations: How do locality, consistency, tenant isolation, operational effort, and total infrastructure cost compare?
Keep the current database approach if it meets measured quality, performance, filtering, and operational needs. If vector retrieval is a core workload and the existing design fails one or more of those requirements, benchmark dedicated services using the same queries, corpus, load, and relevance checks before choosing one. The result should be a workload-based decision, not a guess based on corpus size alone.
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