Topic
6 articles on Vector Databases, written from production systems rather than documentation.
Benchmarks compare query speed at a scale you may never reach. Filtering, tenancy and how updates behave decide more of the outcome than throughput.
Below a few hundred thousand vectors, a dedicated database mostly adds a system to operate. The threshold is higher than the tooling implies.
Approximate search is approximate on purpose. Knowing which results you are giving up explains retrieval failures that look like model problems.
Real queries are never purely semantic. They are scoped to a tenant, a status and a date, and where that filter runs changes the results you get.
Storing embeddings beside the rows they describe removes a sync problem and makes filtering a join. That is worth more than raw index throughput.
Approximate indexes want to be in memory, and memory is the bill. Sizing that before you commit is the difference between a plan and a surprise.
Whether it needs designing from scratch or rescuing from its own success, tell me what you’re building and I’ll tell you how I’d architect it.
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