Python's ecosystem is hard to beat, but Elixir/Nx already does a lot of what Mojo promises. EXLA gives you GPU/TPU compilation through XLA with similar performance to Mojo's demos, Explorer handles dataframes via Polars, and now Pythonx lets you embed Python when you need those specialized libraries.
The real difference is that Elixir was built for distributed systems from day one. OTP/BEAM gives the ability to handle millions of concurrent requests as well as coordinating across GPU nodes. If you're building actual ML services (not just optimizing kernels), having everything from Phoenix / LiveView to Nx in one stack built for extreme fault-tolerance might matter more than getting the last bit of performance out of your hardware.
I recently built out training and inference at a FinTech (for fraud and risk) using Elixir and tried this very approach…
We’re now using Python for training and forking Ortex (ONNX) for inference.
The ecosystem just isn’t there, especially for training. It’s a little better for inference but still has significant gaps. I will eventually have time to push contributions upstream but Python has so much momentum behind it.
Livebooks are amazing though and a better experience than anything a Python offers sans libraries.
The real difference is that Elixir was built for distributed systems from day one. OTP/BEAM gives the ability to handle millions of concurrent requests as well as coordinating across GPU nodes. If you're building actual ML services (not just optimizing kernels), having everything from Phoenix / LiveView to Nx in one stack built for extreme fault-tolerance might matter more than getting the last bit of performance out of your hardware.