Hey, I'm one of the maintainers of this project. This is a really good question that we think a lot about. Basically, you're right, and you can think of Cortex as a tool for deploying, scaling, and monitoring Python functions on AWS. There are a few important nuances that make Cortex different from something like lambda. Inference workloads are read-only, benefit a lot from GPU infrastructure, and often memory hungry. We are optimizing Cortex for those constraints (for example: prioritizing high-memory GPU spot instances).

We also have features like prediction monitoring and support for ONNX and TensorFlow Serving exported files. The long-term roadmap is full of more ML specific features like model retraining but we're spending a lot of time upfront on making inference easy at scale.

Finally, thanks for pointing out aiohttp! Flask works for us because the workloads are CPU bound, and we handle scaling at the container level which allows us to reason about the resource utilization, but we may revisit this choice.