I have helped a number of teams pick a vector database in the last year. The conversation always starts the same way: four logos, one Slack message, one question — which one?
The honest answer is that all four are good. The useful answer is that each one is built for a different set of constraints. Getting this decision wrong does not break your application on day one. It shows up six months later when your bill is three times your compute budget, or your filtered search recall starts degrading under load.
This post gives you the architecture overview, latency benchmarks, filtering quality comparison, hybrid search comparison, real cost numbers, and code examples for each. The full 4,000-word breakdown with detailed cost formulas and every edge case is at krunalkanojiya.com.
The Short Answer First
Skip the full read if you already know your constraints:
