ONNX

What is ONNX?

ONNX, short for Open Neural Network Exchange, is an open format for representing machine learning models. It defines a common set of operators and a common file format so a model built in one framework can be used with other frameworks, tools, runtimes and compilers. It is not a hosted product or an agent: it is a specification and ecosystem maintained as a community project, and the site lists it as an LF AI graduate project.

Key capabilities of ONNX

• Common operator definitions: a shared set of building blocks that models are expressed in.
• Standardized model file format: one file format that tools across the ecosystem can read and write.
• Framework interoperability: build in a preferred framework and run with a chosen inference engine.
• Hardware-optimized inference: ONNX-compatible runtimes and libraries aim to maximize performance across hardware.
• Open governance: special interest groups and working groups, with an open contribution process.

How ONNX works

A team trains or exports a model from a framework such as PyTorch or TensorFlow into the ONNX format. The exported file can then be loaded by an ONNX-compatible runtime such as ONNX Runtime, which executes the model on the target hardware. The site links to a supported-tools page covering both model-building frameworks and deployment accelerators.

Who uses ONNX?

ML engineers and AI developers use ONNX when training and serving happen in different tools, or when the same model must run on different hardware. Contributors can join working groups or follow the contribution guide on the project site.

ONNX pricing

ONNX is open source and free to use. The project site does not describe paid tiers. Costs come from the infrastructure you run models on and from any commercial runtime or accelerator you choose.

ONNX alternatives

ONNX is a model format rather than a model developer or a cloud, so the listed alternatives are adjacent rather than direct. Sakana AI and Liquid AI develop their own AI models and architectures, which you might deploy alongside or instead of converting models. Nebius and Hyperbolic provide AI cloud and GPU infrastructure where ONNX-exported models can be served.