Go is rarely the right choice for ML inference because it lacks native support for high-performance tensor operations and hardware acceleration libraries like CUDA or ROCm. While you can use Go to wrap C++ inference engines (like ONNX Runtime or TensorFlow C API) via cgo, the language itself does not provide the low-level memory control or optimized math kernels required for efficient model serving. For most production ML workloads, Python with optimized backends or C++ remains the standard.
Go for ML Inference
When It Makes Sense
Go is not ideal for ML inference due to a lack of native tensor libraries and hardware acceleration support, though it can wrap C++ engines.