PgFlow is pretty awesome for DAG workflows - it's built on pgmq (which does the heavy lifting, making it backend agnostic).

Typescript: https://www.pgflow.dev

Elixir: https://github.com/agoodway/pgflow/blob/main/docs/COMPARISON...

Temporal is an insane piece of software, always surprised people dont know about it. You could replace almost youre whole AWS stack with temporal

This feels like the sort of architecture that starts clean and then gradually grows most of the things a workflow-native system already has. I've seen systems like this, seen companies that are built out of this idea, and built small systems like this over time.

Once you need retries, backoff, timeouts, cancellation, versioning, visibility, task routing, rate limits, leases, heartbeats, stuck-worker detection, replay/debugging semantics, workflow migration, fanout/fanin, long timers, audit trails, and operator tooling, the “just use a database” story becomes “build a poor copy of a workflow engine plus a bunch of workers.” pretty quick.

That may still be a good tradeoff for many applications, especially if Postgres is already the core operational dependency. But the comparison shouldn’t be “database vs overcomplicated orchestrator.” It’s more like “what complexity do you want to own, and what do you want to buy / offload to a professional system?”