ONNX Runtime
ONNX Runtime: A free, open-source runtime for executing and optimizing ONNX models across devices. Ranked #30 of 35 in Deep Learning Software by our editors (5.1/10); pricing: Free plan; best for teams running ONNX models across devices.
At a glance
- Editor score5.1 / 10
- PricingFree plan
- Best forTeams running ONNX models across devices
- Free planYes
- Paid fromNone
- Training modeLocal
- Facts checked23 Sep 2026
Where it wins
- Runs optimized models across cloud, edge, mobile and web targets
- Supports CPU, GPU, CUDA and TensorRT execution providers
- Offers on-device training and APIs across many languages
Where it doesn't
- Primarily an inference runtime, not a full model-development suite
- Training support centers on large-model and on-device scenarios
- Support is provided through documentation and community channels
Our verdict on ONNX Runtime
ONNX Runtime is Microsoft's open-source runtime for loading and running ONNX models, including models converted from PyTorch, TensorFlow/Keras, TFLite and scikit-learn. It is aimed at teams that need one execution layer across cloud, self-hosted, mobile, web and edge deployments. The runtime optimizes model graphs and partitions workloads for hardware-specific execution providers, with CPU and GPU support including NVIDIA CUDA and TensorRT. APIs and packages cover Python, C, C++, C#, Java, JavaScript, TypeScript, Kotlin and Objective-C.
Its ecosystem fit is a central strength. Developers can bring models from several established frameworks, convert ONNX models to the reduced-size ORT format, and select packages for Windows, macOS, Linux, iOS, Android and web environments. The platform also supports on-device training: an offline phase prepares training artifacts for later device-side updates, which suits personalization and federated-learning scenarios. This combination makes it practical when the same model must run across different hardware and application stacks.
The trade-off is scope. ONNX Runtime's primary role is inference execution, while its documented training capabilities focus on large-model and on-device scenarios rather than providing a complete model-development workspace. Teams should choose it when cross-platform deployment, hardware-aware optimization and local training support are priorities. Organizations seeking an end-to-end environment for developing and training models may need a more comprehensive tool. As open-source software with a free plan, it avoids license charges, while support is centered on documentation and community channels.
ONNX Runtime pricing
ONNX Runtime fact sheet
| Free plan | Yes |
|---|---|
| Paid from | None |
| Training mode | Local |
| Deployment targets | Multiple |
| GPU acceleration | Yes |
| Distributed training | Not verified |
| Supported languages | Python, C, C++, C#, Java, JavaScript, TypeScript, Kotlin, Objective-C |
| Model formats | ONNX, ORT |
| Deployment | Cloud, Self-hosted, Mobile |
| Platforms | Web, Windows, macOS, Linux, iOS, Android |
| Support | Community, Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 4 integrations: PyTorch, TensorFlow/Keras, TFLite, scikit-learn |
| Pricing | Free plan |
| Website | onnxruntime.ai |
| Facts checked | 23 Sep 2026 |
ONNX Runtime integrations
ONNX Runtime lists 4 integrations on its own site.
- PyTorch
- TensorFlow/Keras
- TFLite
- scikit-learn
Alternatives to ONNX Runtime
- Amazon SageMaker AIA comprehensive AWS-managed ML workflow suite for teams that accept usage-based billing.9.0
- Azure Machine LearningA paid Azure-native ML lifecycle service spanning automated training, deployment, and MLOps.7.8
- CaffeA free, established framework for teams maintaining Caffe-based model workflows.7.7
See all ONNX Runtime alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026
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