Vector databases have become an essential component of modern AI infrastructure. They are the storage that enables semantic search, RAG systems, and recommendation systems.
What is a vector database?
A vector database stores and searches vectors (arrays of numbers representing data meaning). Unlike traditional databases that search for exact matches, vector databases search by semantic similarity.
If you search for "comfortable running shoes," a vector database finds results with similar meaning even if they use different words.
How do they work?
Data (text, images, audio) is converted to vectors using embedding models. These vectors are stored in the vector database with specialized indexes (HNSW, IVF) that enable similarity searches in milliseconds.
Main solutions
Pinecone: Fully managed, no infrastructure operations. Ideal for startups and small teams. Scalable to millions of vectors.
Weaviate: Open-source with cloud option. Supports hybrid search (vector + keyword). Good for teams wanting control.
Qdrant: Open-source, very fast, written in Rust. Ideal for applications requiring low latency.
Chroma: Open-source, simple, ideal for development and prototyping. Not recommended for large-scale production without additional management.
Milvus / Zilliz: The most scalable solution (billions of vectors). Used by companies like eBay and Walmart.
Use cases
RAG: The vector database stores document chunks and enables retrieving the most relevant ones for each query.
Semantic search: Search products, articles, or documents by meaning, not exact keywords.
Deduplication: Find duplicate or similar content in large data volumes.
Recommendation systems: Find similar items by user preference embeddings.
How to choose
To start: Chroma or Pinecone. For production with control: Weaviate or Qdrant. For scaling to billions: Milvus. For existing data integration: PostgreSQL + pgvector.
Distance metrics
Most common: cosine similarity (text), euclidean distance (numerical data), dot product (recommendation systems).
Vector databases are the key to AI applications that understand meaning. At Vynta we design and implement systems with vector databases for semantic search and RAG. Contact us for your project.