The full answer
A vector database stores embeddings, the numerical representations of text, images or other content, and finds the items most similar to a query. It is the retrieval layer behind RAG assistants, semantic search and recommendation features.
pgvector adds vector search to PostgreSQL. For many products it avoids running a separate system, lets you join vector results with ordinary business data in one query and keeps backups, access control and data residency in one place. Creuto starts here unless there is a reason not to.
Pinecone is a fully managed service that scales to very large indexes without infrastructure work. It fits products with high query volumes or rapidly growing content where the team would rather not tune a database.
Weaviate is open source and can run in your own cloud account, which matters when data must not leave your environment. It also supports hybrid search that combines keyword and semantic matching, which often improves retrieval quality on business documents.
Retrieval design matters more than the brand of store: chunking, metadata filters, hybrid ranking and permission checks decide whether answers are accurate. Creuto evaluates retrieval quality on your own documents before committing, and can migrate between stores later because the application is built behind an abstraction.