Silo, pool, or bridge is one of the few architecture choices you cannot cheaply reverse. Here are the three models in plain terms, how they map to a real database, and a decision framework so you pick the one your customers and your margins actually need.
Most database optimization guides hand you a list of ten techniques and skip the one question that matters: which problem do you actually have? Here is the diagnosis-first version. Find the real bottleneck, then apply the cheapest fix that clears it.
I benchmarked both. Locally, Redis at 0.86ms was no faster than hitting the database. Move the database away and it wins. How to pick, with numbers.
Most teams reach for sharding years before they need it. There is a ladder of cheaper, lower-risk fixes that comes first, and the right rung depends on which bottleneck you actually have: read load, write load, or data size. Here is the ladder, from cheapest to last resort.
Both are excellent databases and for most SaaS workloads either one is fine. The honest answer: pick Postgres for greenfield, keep what you run well, and switch only for a reason you can name. Here are the few differences that actually decide it.
A government directive can now switch off your AI provider overnight - and pricing, deprecation, and policy could always do it. If a core feature depends on one model API you don't control, that's a single point of failure. Here's how to treat model access like any other supply-chain risk.
