Most use-cases and people/companies don't have the traffic amount where scaling matters that much, so performance gains will be negligible (or even worse) when using a more complex "specialized" stack.
Yes, if you have a large team, building for scale in a VC-funded company, go for dedicated stacks, but if you are just building a start-up where moving fast is important and architecture quickly changes, any DB will do and will not be the bottleneck.
I use Postgres to cache some responses from APIs (max 100 different documents per month) where latency doesn't matter, so using Redis instead would provide no benefit (will actually make it worse, due to its "worse" persistency and third-party integrations capabilities)
Most use-cases and people/companies don't have the traffic amount where scaling matters that much, so performance gains will be negligible (or even worse) when using a more complex "specialized" stack.
Yes, if you have a large team, building for scale in a VC-funded company, go for dedicated stacks, but if you are just building a start-up where moving fast is important and architecture quickly changes, any DB will do and will not be the bottleneck.
I use Postgres to cache some responses from APIs (max 100 different documents per month) where latency doesn't matter, so using Redis instead would provide no benefit (will actually make it worse, due to its "worse" persistency and third-party integrations capabilities)