Vector Database

June 2, 2026
|By Julia Schönau

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A vector database is a specialized database designed to store and search embedding vectors. It serves as the technical backbone of any semantic search and is therefore a central component of modern knowledge AI in voice and chat applications. For a chatbot or voicebot with a substantial knowledge base, the choice and configuration of the vector database directly influence response quality, latency, and operating costs.

 

What Sets Vector Databases Apart from Traditional Databases

Relational databases work with precise values and exact joins. A vector database, on the other hand, stores high-dimensional vectors and supports nearest-neighbor searches. To do this, vector databases use approximate algorithms such as HNSW, IVF, or PQ, which enable extremely high speeds but also introduce a slight, controlled loss of quality.

 

Common options on the market

  • Specialized vector databases: Pinecone, Weaviate, Qdrant, Milvus.
  • Extensions for traditional databases: pgvector for PostgreSQL, Elasticsearch with vector search.
  • Cloud-native services: Vertex AI Matching Engine, Azure AI Search, Amazon OpenSearch.

BOTfriends selects the vector database on a case-by-case basis for each use case—with scalability, EU hosting, filtering capabilities, and integration with the existing platform being key factors.

 

Vector Database in the RAG Pipeline

A typical RAG pipeline consists of three steps: document chunking, generating the embeddings , and storage in the vector database. When a query is made, the query itself is embedded, the vector database returns the top hits, and a reranker determines the final ranking. Only this combined stack enables Semantic Search at a production-ready level.

 

Scaling, Filtering, and Governance

High-performance vector databases must do more than just perform nearest-neighbor searches. Key features include metadata filters (such as language, client, and date), multi-tenancy for different client contexts, and a clear authorization model. For BOTfriends, EU hosting is mandatory, as are auditable logs and a clearly defined deletion process. This ensures the platform remains GDPR-compliant while maintaining high performance.

 

Frequently Asked Questions (FAQ)

Not necessarily. Specialized systems are only worthwhile once you reach a certain volume.

This is very important. Client filters, language filters, and document date filters transform a generic search into a Knowledge AI system that can be used productively.

That depends on the number and dimensionality of the vectors. Embedding quantization can significantly reduce memory requirements.

Yes. Different vector spaces can be hosted on the same platform, for example, for different languages or use cases. It is important to maintain clear separation and versioning.

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