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I've been using Temporal recently for some long-running multi-step AI workflows -- helps me get around API flakiness, manage rate limits for hosted models, and manage load on local models. It's pretty cool to write workers in different languages and run them on different infra and have them all orchestrate together nicely. How does DBOS compare -- what are the core differences?

From what I can tell, the programming model seems to be pretty similar but DBOS doesn't require a centralized workflow server, just serverless functions?


Co-founder here:

Great question! Yeah, the biggest difference is that DBOS doesn't require a centralized workflow server, but does all orchestration directly in your functions (through the decorators), storing your program's execution state in Postgres. Implications are:

1. Performance. A state transition in DBOS requires only a database write (~1 ms) whereas in Temporal it requires a roundtrip and dispatch from the workflow servers (tens of ms -- https://community.temporal.io/t/low-latency-stateless-workfl...). 2. Simplicity. All you need to run DBOS is Postgres. You can run locally or serverlessly deploy your app to our hosted cloud offering.

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