Find AI Tools
ONNX logo

ONNX

Open standard for machine learning model interoperability.

Get ONNX
  • Always free
  • No credit card
ONNX screenshot

What is ONNX ?

ONNX (Open Neural Network Exchange) is an open standard format for representing machine learning models. It allows you to build models in one framework (such as PyTorch or TensorFlow) and deploy them using different inference engines and tools without being locked into a single ecosystem. This means your team can choose the best framework for training and the best runtime for production deployment, without costly model conversion or compatibility issues. ONNX is maintained by a community of contributors and backed by major AI companies, making it a practical choice for organisations that want flexibility in their ML pipelines.

Key features

Standard model format

Define models using a common set of operators that work across different frameworks and tools

Cross-framework compatibility

Export models from PyTorch, TensorFlow, scikit-learn and other frameworks; import them into any ONNX-compatible runtime

Hardware optimisation

Deploy models with runtimes that are optimised for CPUs, GPUs, and specialised AI accelerators

Model versioning and validation

Tools to check model correctness and track versions across your workflows

Open governance

Community-driven development with transparent decision-making and vendor neutrality

Pros & cons

Advantages

  • Avoid vendor lock-in by separating model development from deployment
  • Access hardware-specific optimisations through multiple runtime options
  • Reduce time spent on model conversion and compatibility troubleshooting
  • Benefit from an active community and growing ecosystem of supporting tools

Limitations

  • Not all custom or experimental layer types may be supported yet, requiring workarounds for bleeding-edge research
  • Requires some technical knowledge to set up and configure runtimes for best performance

Use cases

Deploying machine learning models trained in one framework to production systems using a different inference engine

Building ML pipelines where different teams use different training frameworks but need compatible outputs

Optimising inference performance by switching runtimes without retraining models

Sharing pre-trained models across organisations whilst ensuring reproducibility and compatibility

Ready to try ONNX ?

Get ONNX

Pricing

Free

Free

Full access to ONNX format specification, converters, and reference implementations. Community support and documentation.

Get started with ONNX

Click through to ONNX and start using it now.

Get ONNX
  • Always free
  • No credit card