Vector Database
Definition
A vector database stores number vectors and supports their similarity search.
A vector database manages embeddings and searches for similar entries based on a suitable distance or similarity measure. Additional metadata can enable filters. Speed and hit quality depend on data, index and configuration.
Simply explained
Like a card box that sorts by content proximity instead of just by keywords.
Why it matters in practice
Important in order to make existing company knowledge findable and traceable usable.
Example from business
Possible example: A team searches for similar support cases from a shared document stock.
Limits and pitfalls
- Neglect access rights, deletion, updating and classic filter search.
Origin & history
Qdrant now documents vector storage, similarity measures and additional filter data. This source proves a concrete implementation; it is not a proof of the origin of vector databases.
Use in mid-sized companies
- Possible application in medium-sized companies: A team searches for similar support cases from a approved document stock.
Frequently asked questions
What does Vector Database mean?
A vector database stores number vectors and supports their similarity search.
What errors should be avoided?
Neglect access rights, deletion, updating and classic filter search.
Sources
- What is Qdrant? · Qdrant, What is Qdrant?
- Collections: Vektoren und Distanzmaße · Qdrant, Collections: Vektoren und Distanzmaße
Sources checked on 09.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: