You can absolutely mount a BentoML service into your own Starlette (or any ASGI framework); `svc.asgi_app` is all you'd need. Instantiating and using a runner

You can absolutely mount a BentoML service into your own Starlette (or any ASGI framework); `svc.asgi_app` is all you'd need. Instantiating and using a runner can be done anywhere with `init_local`, but it's really the runner ASGI app that does the work of queuing and batching. We've thought about allowing users to spin that app up separately but it's not a focus right now; instead we're trying to ensure that the system is as easy to use for data scientists as possible and have that workflow fully ironed out before we support the more advanced use-cases. The whole runner situation is quite complex because we wanted to support user-created runners in the nicest way possible, and also leave the space open for non-python runners (and service app) in the future.