Out of curiosity, how is the 92% recall calculated? For a given query, is the recall compared to the true topk of all 100B vectors vs. recall at each of N shards compared to the topk of each respective shard?
I've seen a decent amount of production use of pgvector HNSW from our customers on GCP, but as the author noted is not without some flaws and are typically in the smallish range (0-10M vectors) for the systems characteristics that he pointed out - i.e. build times, memory use. The tradeoffs to consider are whether you want to ETL data into yet another system and deal with operational overhead, eventual consistency, application-logic to join vector search with the rest of your operational data. Whether the tradeoffs are worth it really depends on your business requirements.
And if one needs the transactional/consistency semantics, hybrid/filtered-search, low latencies, etc - consider a SOTA Postgres system like AlloyDB with AlloyDB ScaNN which has better scaling/performance (1B+ vectors), enhanced query optimization (adaptive pre-/post-/in-filtering), and improved index operations.
Full disclosure: I founded ScaNN in GCP databases and currently lead AlloyDB Semantic Search. And all these opinions are my own.
The alternative is to find solutions that can reasonably support different requirements because business needs change all the time especially in the current state of our industry. From what I’ve seen, OSS Postgres/pgvector can adequately support a wide variety of requirements for millions to low tens of millions of vectors - low latencies, hybrid search, filtered search, ability to serve out of memory and disk, strong-consistency/transactional semantics with operational data. For further scaling/performance (1B+ vectors and even lower latencies), consider SOTA Postgres system like AlloyDB with AlloyDB ScaNN.
Full disclosure: I founded ScaNN in GCP databases and am the lead for AlloyDB Semantic Search. And all these opinions are my own.