This looks decent for what it is. I feel like there are umpteen solutions for easy self-hosted compute (and tbh even a plain Linux VM isn't too bad to manage). The main reason to use a PAAS provider is a managed database with built-in backups.
Its the flexibility and power of Kubernetes that I think is incredible. Scaling to multiple nodes is trivial, if your entire data plane is blown away, the recovery is trivial.
You can also self host almost any open source service without any fuss, and perform internal networking with telepresence. (For example, if you want to run an internal metabase that is not available on public internet, you can just run `telepresence connect`, and then visit the private instance at metabase.svc.cluster.local).
Canine tries to leverage all the best practices and pre-existing tools that are already out there.
But agreed, business critical databases probably shouldn't belong on Kubernetes.
Fully agreed - our recommendation is to /not/ run your prod Postgres db yourself, but use one of the many great dedicated options out there - Crunchy Data, Neon, Supabase, or AWS RDS..!
It really depends upon how much data you have. If its enough to just dump then go crazy. If it isn't its a bit more trouble.
Regardless, you're going to have a much easier time developing your app if your datastore access latency is submillisecond rather than tens of milliseconds.
You're running at a pretty small scale if running your database locally for sub-milisecond latency is practical. The database solution provided by the DBA team in a data center is going to have about the same latency as RDS or equivalent. Typical intra-datacenter network latency alone is going to be 1-3ms.
They were talking about using things like Supabase, not just RDS.
Also, "small scale" means different things to different people. Given the full topic at hand, I would call it "nano scale". Depending upon your specific schema, you can serve tens of thousands of queries per second with a single server on modern hardware, which is way more than enough for the vast majority of workloads.
