Postgres. NoSQL databases are specialized databases. They are best-in-class at some things, but generally that specialization came at great cost to their other

Postgres. NoSQL databases are specialized databases. They are best-in-class at some things, but generally that specialization came at great cost to their other options. DynamoDB is an amazing key-value store, but is severely limited at everything else. Elasticsearch is an amazing for search and analytics, but is severely limited at everything else. Other specialized databases that are SQL-full are also great at what they do, like Spark is a columnar database that has amazing capabilities for massive datasets where you need lots of cross-joins, but that severely limits it's ability to act in a lot of roles, because they traded latency for throughput and horizontal scalability, and you're restricted in what you can do with it. The super-power of Postgres is that it supports everything. It's a best-in-class relational database, but it's also a decent key-value store, it's a decent full-text search engine, it's a decent vector database, it's a decent analytics engine. So if there's a chance you want to do something else, Postgres can act as a one-stop-shop and doesn't suck at anything but horizontal scaling. With partitioning improving, you can deal with that pretty well. If you're writing fresh, there is basically no reason not to use Postgres to start with. It's only when you already know your scale won't work with Postgres that you should reach for a specialized database. And if you think you know because of published wisdom, I'd recommend you set up your own little benchmark, generate the volume of data you want to support, and then query it with Postgres and see if that is fast enough for you. It probably will be.