What does it do?
ONNX, short for Open Neural Network Exchange, is a shared file format for AI models, so a neural network trained in PyTorch can be exported and run inside a completely different system without retraining. It’s less a tool you interact with directly and more a translation standard, similar to how a PDF opens on any computer regardless of which program created it. Without a shared format like this, a model built in one framework would often be stuck there, unable to run efficiently on the hardware or software a company actually wants to deploy on. Backed jointly by Microsoft and Meta, it’s become the common handshake between the AI training world and the AI deployment world.
See it in action
This code opens a previously saved AI model file and checks that it was built correctly so it can safely run somewhere else.
import onnx
model = onnx.load("path/to/model.onnx")
onnx.checker.check_model(model)
print("Model is valid:", model.graph.name)Why would a non-developer care?
Training an AI model and actually running it cheaply and fast inside a real product, sometimes on a phone or a small chip, are two very different engineering problems, and ONNX is what lets teams solve them with different, specialized tools instead of being locked into one framework end to end.
Real-world examples
Companies deploying AI models to phones, embedded devices, or specialized inference hardware often convert their models to ONNX first, because ONNX runtimes are optimized for speed in ways general-purpose training frameworks aren’t. Microsoft uses it extensively across its own products to run models efficiently at scale.
Who uses it
Machine learning engineers responsible for deploying trained models into production systems, especially on constrained hardware like mobile devices.
How it compares to alternatives
There isn’t a true one-to-one competitor, since ONNX solves a specific interoperability problem, but it overlaps with framework-specific export tools like TensorFlow’s SavedModel or PyTorch’s TorchScript, both of which lock a model into that framework’s own ecosystem rather than making it broadly portable.
Fun fact
ONNX was jointly launched by Microsoft and Facebook, now Meta, in 2017, a rare case of two major tech rivals collaborating on a shared open standard rather than competing with incompatible formats.