Milvus / Zilliz Cloud vs pgvector

Milvus scales to trillions of vectors using hierarchical index structures with tiered storage, serving billion-scale collections that exceed single-machine memory limits via Zilliz Cloud managed deployment.

pgvector stores embeddings as a native column type and queries them with standard SQL, avoiding the operational complexity of a separate vector database for applications already running on Postgres.

Milvus GPU indexing builds IVFPQ indexes on billion-vector collections 10x faster than CPU-only builds, reducing the time from ingestion to searchable index for large-scale embedding pipelines.

pgvector's HNSW index achieves sub-50ms similarity search for collections under 10M vectors, covering most product recommendation and semantic search use cases without a specialized vector database.