Verdict: Python first, then Rust
Rust vs Go for ML inference:
Rust if you must, but Python is the right answer.
Python's ML ecosystem (PyTorch, JAX, Hugging Face) is unbeatable for training and most inference. Rust wins for production inference servers where latency is measured in single-digit milliseconds.
Python
USE FOR
Model training, research, prototyping, anything with Jupyter. Use this 90% of the time.
NOT FOR
Sub-10ms latency serving, compiled inference with no Python overhead
Rust
USE FOR
Production inference server with candle or burn. Latency-critical serving at high QPS. Edge inference on WASM.
NOT FOR
Model training, anything requiring PyTorch gradients or JAX JIT
Go
USE FOR
Serving wrapper around a Python inference server. Orchestration and routing layer.
NOT FOR
Actual ML inference. Go has no competitive ML numerical library.
Rust ML inference libraries (2026)
candleby Hugging Face
Minimalist ML framework from Hugging Face, written in Rust. Can load Safetensors and run inference on CPU and CUDA. The most production-ready Rust ML option.
burnby community
Full deep learning framework in Rust with support for multiple backends (NdArray, LibTorch, Candle, WGPU). More ambitious than candle but less battle-tested.
ort (ONNX Runtime)by Microsoft
Rust bindings for ONNX Runtime. Most mature option if you have trained models in PyTorch or TF and want to serve them with a Rust binary.