| This quite aligns with our observation at Milvus. Recently, we helped several users migrate from pgvector as the workload grew substantially. It’s worth recognising the strengths of pgvector: • For small-to-medium scale workloads (e.g., up to millions of vectors, relatively static data), embedding storage and similarity queries inside Postgres can be a simple, familiar architecture. • If you already use Postgres and your vector workloads are light (low QPS, few dimensions, little metadata filtering / low concurrency), then piggy-backing vector search on Postgres is attractive: minimal added infrastructure. • For teams that don’t want to introduce a separate vector service, or want to keep things within an existing RDBMS, pgvector is a compelling choice. From our experience helping users scale vector search in production, several pain-points emerge when scaling vector workloads inside a general-purpose RDBMS like Postgres: 1. Index build / update overhead
• Postgres isn’t built from the ground-up for high-velocity vector insertions plus large-scale approximate nearest neighbour (ANN) index maintenance, for example, lacking RaBitQ binary quantization supported in purpose built vector db like Milvus. • For large datasets (tens/hundreds of millions or beyond), building or rebuilding HNSW/IVF indices inside Postgres can be memory- and time-intensive. • In production systems where vectors are continuously ingested, updated, deleted, this becomes operationally tricky. 2. Filtered search • Many use-cases require combining vector similarity with scalar/metadata filters (e.g., “give me top 10 similar embeddings where user_status = ‘active’ AND time > X”). • Need to understand low level planner to juggle pre-filtering, post-filtering, and planner’s cost model wasn’t built for vector similarity search. For a system not designed primarily as a vector DB, this gets complex. Users shouldn't have to worry about such low level details. 3. Lack of support for full-text search / hybrid search • Purpose built vector db such as Milvus has mature full-text search / BM25 / Sparse vector support. |