Overview
Description
ONNX Runtime accelerates machine learning inference and training across virtually any hardware, reducing infrastructure costs and improving application responsiveness. It works with models from PyTorch, TensorFlow, scikit-learn, and other major frameworks without requiring deep infrastructure changes. The result: faster ML performance in production with fewer resources.
Technical scorecard
License
MIT
Commercial use
Yes
OpenAI-compatible API
No
REST API
No
Fine-tuning support
Yes
Quantization support
Yes
Docker available
Yes
GUI / no-code available
No
Telemetry
Opt-out
Offline after setup
Yes
Data & Privacy
Does it send data online?
After setup, this listing is marked as usable offline. Confirm network behavior against the upstream project before regulated deployment.
Does it store history?
Not verified in this directory yet. Review the upstream docs for persistence, logs, and workspace storage.
License checks?
Commercial use is marked as allowed or likely allowed by the listed license.
Telemetry?
Opt-out
Last verified: Jul 22, 2026. Maintainer verification should be treated as directory guidance, not legal advice.
Deploying ONNX Runtime in production?
Stop silent telemetry leaks before they trigger an unintended data leak. Get our Airgap Certainty Blueprint for ONNX Runtime.
Request Privacy and Telemetry AuditSetup & Installation
A developer can usually get this running with standard docs.
# Start with the official project repository
# https://github.com/microsoft/onnxruntimeHardware Requirements
Works Well With