A Practical Guide to Vector Databases
Vector databases have moved from a research curiosity to core infrastructure for retrieval-augmented applications in only a few short years.
At their core, vector databases index high-dimensional embeddings so nearest-neighbour lookups stay fast as the corpus grows, trading exact matches for speed at scale.
Benchmarks published this year show retrieval latency holding flat well past a billion vectors on commodity hardware, which is what finally made them production-ready.
The trade-offs are real: index build time, memory pressure, and the cost of re-embedding whenever the model changes.