How do vector databases scale to billions of embeddings?

They use approximate nearest neighbor algorithms, sharding, and compression techniques.

Scaling vector search requires partitioning embeddings using clustering (IVF), graph-based indexing (HNSW), and compression (PQ). Distributed systems shard indexes across nodes and use hierarchical routing to reduce search space.