ONNX Runtime vs PaddlePaddle
ONNX Runtime
onnxruntime.aiPaddlePaddle
paddlepaddle.org.cnSide by side
| Figure | ONNX Runtime | PaddlePaddle |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | Yes | — |
| Android | Yes | — |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Deep Learning Software | 8.4 (#6) | 7.4 (#24) |
| 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. | Paddle Inference documents integrations with TensorRT, cuDNN, oneDNN, and Paddle Lite. |
| Maker | The site identifies Microsoft in its copyright notice; the pages reviewed do not state headquarters or a founding date. | The project’s official GitHub repository identifies PaddlePaddle as its core framework; Baidu’s investor FAQ lists its headquarters as Beijing and says it was incorporated in 2000. |
| Purpose | ONNX Runtime is a cross-platform machine-learning model accelerator with interfaces for hardware-specific libraries. | PaddlePaddle describes itself as an efficient, flexible, extensible deep learning framework intended to make deep learning innovation and application easier. |
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.
PaddlePaddle
PaddlePaddle has no write-up on Specifiction yet; its figures are above.