A vector database stores embeddings so systems can retrieve information by semantic similarity.
Vector databases are useful when you want semantic retrieval across documents, code, or unstructured content. They are a core building block in many RAG systems.
Their strength is similarity search, not durable user memory or state management by itself.
A memory layer needs more than embeddings. It needs scope boundaries, write and retrieval policies, time-aware behavior, and a product story around continuity across sessions.
That is why buyers increasingly compare vector infrastructure with memory layers instead of assuming one automatically replaces the other.
Use these glossary pages and commercial landing pages to move from definition to implementation.