TL;DR
Vector databases store data as high-dimensional vectors (embeddings), enabling semantic search — finding results by meaning, not just keywords.
- Core use case: Power RAG systems, recommendation engines, and semantic search
- Top options: Pinecone (managed), Weaviate (open source), ChromaDB (lightweight), Qdrant (Rust-based)
- How it works: Text → Embedding model → Vector → Stored in DB → Similarity search via cosine/dot product
Bottom Line: If you’re building any AI application that needs to search or retrieve information, you need a vector database.
If you want to create AI applications such as a semantic search, you need a suitable vector database. These make embeddings available at lightning speed and scale better than classic SQL or NoSQL stores. We present popular options from SaaS to open source – including strengths, costs and typical use cases.
