Vector databases index embeddings so applications can find related documents quickly. Popular options like Pinecone, Weaviate and Qdrant handle billions of vectors with sharding, filtering and hybrid search.
Choosing a database depends on performance requirements, open-source vs hosted options, and integrations with machine learning frameworks. Benchmarks and documentation help teams select the best fit.