Answers
What is a vector database?
A vector database is a system built to store, index, and search embeddings, the numeric representations of text, images, or audio. Instead of matching keywords, a vector database ranks results by mathematical closeness. Teams use a vector database to power semantic search, recommendation engines, and retrieval for AI assistants.
01How does a vector database work?
A vector database converts each record into a list of numbers called an embedding, then stores the numbers alongside an index built for fast similarity search. When a query arrives, the query gets embedded too, and the database returns records whose vectors sit closest by distance measures such as cosine similarity or dot product.
Most vector databases pair an approximate nearest neighbor index with metadata filters, so a search can combine semantic closeness with hard constraints like date range, category, or owner.
02Why do companies need a vector database?
Companies need a vector database when keyword search fails to capture meaning, for example matching a support ticket to a help article that uses different wording. A vector database also supplies the retrieval step in retrieval augmented generation, feeding relevant passages to a language model so answers stay grounded in real documents.
Without a dedicated index, similarity search across millions of records becomes slow and memory heavy, which pushes teams toward purpose built storage.
03What is the difference between a vector database and a relational database?
A relational database organizes rows and columns and answers exact match or range queries using SQL. A vector database organizes high dimensional vectors and answers nearest neighbor queries. The two solve different problems, and production systems often run both side by side, with the relational database holding structured records and the vector database holding embeddings for semantic lookup.
04Which vector databases are commonly used?
Common options include open source projects such as Milvus, Weaviate, and Qdrant, managed cloud services such as Pinecone, and vector extensions added to existing databases such as Postgres with pgvector. Selection depends on scale, latency needs, operational preference for managed versus self hosted, and how tightly the vector store must integrate with existing data pipelines.
05What are common problems with a vector database deployment?
Common problems include poor embedding quality, stale indexes after source documents change, oversized chunks that dilute relevance, and missing metadata filters that let irrelevant results through. Teams also underestimate reindexing cost when the embedding model changes, since every stored vector must be regenerated to stay compatible with new query embeddings.
06Specification
| Spec | Detail |
|---|---|
| Typical cost | Ranges widely by scale, from free open source self hosting to a few hundred to several thousand dollars a month for managed services handling millions of vectors. |
| Typical timeline | A basic proof of concept can run in one to two weeks, production grade retrieval pipelines typically take four to ten weeks. |
| Prerequisites | A source document set, an embedding model, and a defined chunking strategy for splitting long text. |
| Common failure modes | Stale indexes, poor chunking, missing metadata filters, and mismatched embedding models between indexing and querying. |
| Who it suits | Teams building semantic search, recommendation systems, or retrieval augmented generation for an AI assistant. |
07Vector database options at a glance
| Option | Deployment model | Best fit |
|---|---|---|
| Pinecone | Managed cloud | Teams that want low operational overhead |
| Milvus | Self hosted or managed | Large scale deployments needing full control |
| Weaviate | Self hosted or managed | Teams wanting built in hybrid search |
| pgvector | Postgres extension | Teams already standardized on Postgres |
FAQQuestions
Related questions
Is a vector database the same as a search engine?
No. A search engine like Elasticsearch traditionally ranks by keyword frequency, while a vector database ranks by geometric distance between embeddings. Some search engines now add vector support, blurring the line, but the underlying ranking method differs.
Can a vector database replace a relational database?
No. A vector database handles similarity search over embeddings, not transactional records, foreign keys, or exact match queries. Production systems typically keep a relational database for structured data and add a vector database for semantic lookup.
How large can a vector database get?
Vector databases scale from a few thousand vectors on a laptop to billions of vectors across distributed clusters, depending on the chosen index type and available memory.
Does a vector database need a GPU?
A GPU speeds up embedding generation and some index building steps, but the vector database itself typically runs on standard CPU infrastructure once the index is built.
NEXTWhere to go next
AI integration is the Lingows AI page that covers this work in delivery detail, including scope, timeline, and how the build is run.
Last reviewed 2026-08-16