ONNX Runtime vs TensorFlow
ONNX Runtime
onnxruntime.aiTensorFlow
tensorflow.orgSide by side
| Figure | ONNX Runtime | TensorFlow |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | Yes | Yes |
| Android | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Deep Learning Software | 8.4 (#6) | 7.5 (#22) |
| Integrations | The ecosystem documentation lists integrations with Azure Machine Learning, Azure Custom Vision, Azure SQL Edge, Azure Synapse Analytics, ML.NET, and NVIDIA Triton Inference Server. | The TFX pipeline tutorial describes exporting pipeline source code that can be orchestrated with Apache Airflow and Apache Beam. |
| Maker | The site identifies Microsoft in its copyright notice; the pages reviewed do not state headquarters or a founding date. | TensorFlow's whitepaper describes the system as built at Google. |
| Support | Documentation questions are directed to issue filing, and the project invites users to report bugs, suggest features, and submit pull requests on GitHub. | TensorFlow directs users to its issue tracker, release notes, Stack Overflow, community forum, and announcement mailing list. |
Plans
In their own words
ONNX Runtime
ONNX Runtime is a free engine for machine-learning inference and training within existing technology stacks. It can run models from frameworks including PyTorch, TensorFlow/Keras, TFLite, and scikit-learn. For inference, it optimizes latency, throughput, memory use, and binary size through graph transformations, accelerator-aware partitioning, and optimized computation kernels. Its Execution Providers connect ONNX models to hardware acceleration libraries for CPUs, GPUs, FPGAs, and specialized NPUs. Listed options include NVIDIA CUDA and TensorRT, Intel OpenVINO, DirectML, Qualcomm QNN, Android NNAPI, Apple CoreML, and WebGPU. Deployment spans cloud servers, edge and mobile devices, and browsers; its web runtime supports browser models and its mobile runtime supports Android and iOS. Supported languages include Python, C#, C++, Java, JavaScript, and Rust. Developers can create smaller web or mobile packages by including only the operators and opsets their models need. The open-source runtime is available on Android, iOS, Linux, macOS, self-hosted environments, web, and Windows.
TensorFlow
TensorFlow is a free, open-source platform for creating machine learning models and running them in different environments. Model-building options include the high-level Keras API, eager execution, and distributed training tools. Models can be deployed on servers, edge devices, and the web: TFX supports production pipelines, TensorFlow Lite supports mobile and edge inference, and TensorFlow.js covers JavaScript environments. The ecosystem also includes LiteRT, tf.data, TensorFlow Datasets, and TensorBoard. TensorFlow provides resources addressing fairness, interpretability, privacy, and security in machine learning workflows, including TF Privacy for privacy-focused training and TF Federated for federated learning. Supported 64-bit environments include Ubuntu, Windows, and macOS; WSL2 GPU support is marked experimental. TensorFlow's install guide states that macOS has no GPU support. Google Colab can run TensorFlow tutorials in browser-based Jupyter notebooks without installation or setup. The API and reference implementation were released under the Apache 2.0 license in November 2015. Listed platforms include Android, iOS, Linux, macOS, self-hosted, web, and Windows.