How to Choose a Vector Database for RAG and AI Search
Choose a vector database by testing retrieval quality, metadata filtering, update behaviour, latency, security and operating cost on the data and queries your application will actually use.
A vector database stores embeddings and retrieves nearby records by similarity. The database is only one part of retrieval: embedding choice, chunking, metadata, hybrid search, reranking and evaluation often affect answer quality more than the vendor name.
Answer in brief
Start with the database and operational skills you already have. Move to dedicated vector infrastructure only when measured scale, filtering, latency, tenancy or operational requirements justify it.
Terminology
'vector store' is the broader informal term. 'Vector database' usually describes a system that combines vector indexing with persistence, metadata, filtering and database operations. Product capabilities change, so verify current vendor documentation during procurement.
Begin With the Retrieval Problem
A vector database is useful when semantic similarity across text, images or other embedded content is an important retrieval signal. It is not automatically required for every AI application. Keyword search, database filters, curated rules or a small approved knowledge base may already solve the task.
Use exact or lexical search when names, identifiers, codes and exact phrases dominate.
Use structured filtering when the answer depends on dates, permissions, status, geography or other explicit fields.
Add semantic retrieval when users express the same idea with different wording or the content is hard to search by exact terms.
Compare hybrid retrieval when both exact matches and semantic meaning matter.
Add reranking only when it produces a measurable improvement on representative queries.
If the corpus is small and the team already operates PostgreSQL, an extension such as pgvector may reduce system count. A dedicated managed or self-hosted vector database becomes more attractive when vector workloads, isolation, filtering or scale deserve their own operational boundary.
Write the Workload Down
Data volume: current records, vector dimensions, growth rate and duplicate content.
Query pattern: expected searches, peak concurrency, result count and response-time target.
Freshness: how quickly additions, edits and deletions must appear in search.
Filtering: permission, tenant, language, product, date and other metadata constraints.
Retrieval: dense, sparse, full-text, hybrid, multi-vector and reranking requirements.
Isolation: tenant boundaries, encryption, region, backup and recovery expectations.
Operations: deployment model, monitoring, upgrades, support and skills available internally.
Cost: storage, writes, queries, replicas, backups, network transfer and engineering time.
The embedding model constrains vector dimensions and may recommend a similarity metric. Store the embedding model and version with each index so the team can rebuild safely. Do not mix incompatible embedding spaces in one search index without an explicit design.
There Is No Universal Best Vector Database
| Option | Useful when | Main trade-off | Questions to test |
|---|---|---|---|
| Relational database extension | The corpus is manageable and the team already operates the database | Vector workload shares resources with transactional data | Filtering plans, index maintenance, backups and peak-query impact |
| Managed vector database | The team wants a service with vector operations handled | Recurring service cost and provider-specific behaviour | Regions, isolation, import/export, limits, support and failure recovery |
| Self-hosted vector database | Infrastructure control or deployment constraints matter | The team owns capacity, upgrades, resilience and incidents | Operational skills, replication, backup, monitoring and upgrade path |
| Search platform with vector support | Lexical, faceted and semantic search must work together | May add search-specific operational complexity | Hybrid ranking, filters, index freshness and relevance tools |
| Cloud data platform capability | Vectors already live beside governed analytical data | Interactive latency or retrieval features may be less specialised | Query performance, serving pattern, governance and cost |
Exact and Approximate Search
Exact nearest-neighbour search checks the eligible vectors directly and provides a useful quality baseline. Approximate indexes such as HNSW or IVFFlat search faster by narrowing the candidate set, which creates a trade-off among recall, latency, memory, build time and update behaviour. Test that trade-off instead of accepting a default configuration.
Metadata filters can change approximate-search behaviour because the system may find similar vectors that are later excluded. Evaluate realistic permission and tenant filters, especially selective filters that leave few eligible records.
Benchmark With Real Queries and Relevance Judgements
Build an evaluation set: Collect representative questions, approved relevant passages and difficult cases, including permission filters and no-answer queries.
Set an exact baseline: Measure the best available retrieval quality before tuning approximate indexes or adding reranking.
Compare configurations: Keep embeddings, chunks and queries fixed while testing one infrastructure choice at a time.
Measure quality and service: Track retrieval recall or relevance alongside latency, throughput, freshness, failures and cost.
Test change: Reindex, update, delete, restore and migrate data in a non-production environment.
Run a limited production trial: Monitor real query patterns and failure cases before committing the full workload.
A Practical Scorecard
| Dimension | Evidence to collect | Why it matters |
|---|---|---|
| Retrieval quality | Recall, judged relevance and grounded-answer evaluation | Fast retrieval is useless when the right evidence is absent |
| Filtering and isolation | Permission tests, tenant leakage tests and filtered recall | RAG must not retrieve content the user cannot access |
| Freshness | Insert, update and delete visibility under load | Stale or undeleted content can create incorrect answers |
| Performance | p50/p95/p99 latency, throughput and error rate | Averages can hide slow or failing requests |
| Operations | Backup, restore, monitoring, upgrades and incident drills | Production reliability includes recovery |
| Cost | Forecast plus measured trial cost at expected traffic | List price rarely captures the complete workload |
Apply access controls before or during retrieval, not only after results return.
Encrypt data in transit and at rest according to the application's requirements.
Separate tenants using a tested design and include isolation checks in evaluation.
Propagate source-document deletions to chunks, vectors, caches and backups according to policy.
Log administrative and data-access events without exposing unnecessary content.
Keep source content, chunk identifiers, metadata and embedding versions outside vendor-specific representations where practical.
Test export and rebuild procedures so the index is replaceable rather than the only copy of the knowledge base.
Illustration: Selecting Infrastructure for an Internal Knowledge Assistant
Illustrative example only - not a Fossilite client result: a company compares its existing PostgreSQL stack with two dedicated vector services. It uses the same documents, embeddings, chunks, permission filters and evaluation queries for every option.
All three retrieve common questions adequately. Selective permission filters and deletion freshness reveal the meaningful differences. The team chooses the option that meets the security and latency requirements with the lowest operating burden, not the product with the longest feature list.
Selecting a database before defining retrieval and governance requirements.
Benchmarking synthetic vectors without real queries, filters and update patterns.
Treating approximate-search speed as the only performance measure.
Ignoring deletion, backup, restore and reindexing behaviour.
Assuming a dedicated vector database will repair poor chunking or weak embeddings.
Comparing vendor features instead of testing the complete workflow.
Vector Database Selection Checklist
The retrieval problem and baseline are documented.
Scale, latency, freshness and filtering requirements are measurable.
Representative evaluation queries and relevance judgements exist.
Exact and approximate retrieval quality have been compared.
Tenant isolation, permissions and deletion behaviour are tested.
Backup, restore, reindexing and migration have been exercised.
Total cost includes infrastructure and operational labour.
The source corpus and index can be rebuilt independently of one provider.
Frequently Asked Questions
What is the difference between a vector store and a vector database?
Vector store is a broad term for infrastructure that stores and retrieves vectors. Vector database usually implies persistence, indexing, metadata, filtering and operational database capabilities, although vendors use the terms differently.
Does every RAG system need a vector database?
No. Small or exact-match knowledge bases may work with lexical search, filters or an existing database. Add vector retrieval when semantic matching improves the evaluated task.
When is pgvector enough?
It can be a practical choice when PostgreSQL is already operated well and the workload fits its tested performance, filtering and availability requirements. Benchmark the real workload before deciding.
What is hybrid search?
Hybrid search combines semantic vector retrieval with lexical or sparse retrieval. It is useful when queries include both concepts and exact terms such as product names, codes or quotations.
What should a vector database benchmark measure?
Measure retrieval relevance, filtered recall, latency percentiles, throughput, update and deletion freshness, failures, recovery and total cost using representative data.
Can a team change vector databases later?
Yes, if it keeps the source content, metadata, embedding versions and identifiers needed to rebuild. Provider-specific schemas and tightly coupled application code make migration harder.