Windows ML
Windows ML: A Windows-native ONNX runtime for private, hardware-accelerated local inference. Ranked #13 of 35 in AI Inference Engines by our editors (7.9/10); pricing: Open source; best for windows-native on-device inference.
At a glance
- Editor score7.9 / 10
- PricingOpen source
- Best forWindows-native on-device inference
- Paid fromFree
- GPU accelerationYes
- Facts checked13 Oct 2026
Where it wins
- Runs ONNX models offline on CPU, GPU, or NPU
- Supports dynamic hardware providers and ONNX conversion tooling
- Offers C#, C++/WinRT, C/C++, and Python APIs
Where it doesn't
- Targets Windows PCs and Windows Server rather than mobile or cross-platform apps
- ONNX is the runtime model format for deployment
- Performance depends on available CPU, GPU, and NPU providers
Our verdict on Windows ML
For teams building Windows applications that need local machine-learning inference, Windows ML provides a Windows-native framework powered by ONNX Runtime. It runs models without an internet connection and can use CPU, GPU, or NPU execution providers, including hardware-specific providers managed by Windows. Support for x64 and ARM64 Windows PCs and Windows Server makes it suitable for desktop software, self-hosted workloads, and deployments ranging from solo developers to enterprise application teams.
Its ecosystem fit is a central strength. Applications can use ONNX models converted from PyTorch, TensorFlow/Keras, TFLite, scikit-learn, and Hugging Face workflows, while ONNX Runtime-compatible APIs ease adoption for existing runtime integrations. Conversion and compilation tooling helps prepare models for deployment, and dynamic acquisition of execution providers lets applications adapt to available hardware. The API surface spans C#, C++/WinRT, C/C++, and Python, giving Windows teams options across native and managed stacks.
Deployment can be self-contained or framework-dependent, allowing teams to balance package control with shared runtime dependencies. Windows ML is distributed through Windows App SDK and NuGet packages, and its MIT license supports open-source use and modification. The trade-off is a focused platform boundary: projects targeting Linux, macOS, or mobile devices need another inference layer, and models must fit the ONNX deployment path. Teams standardizing on Windows hardware and offline inference should find the scope well aligned; cross-platform products or teams seeking a broader set of runtime model formats should consider alternatives.
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Windows ML pricing
Windows ML fact sheet
| Free plan | Not verified |
|---|---|
| Paid from | Free |
| GPU acceleration | Yes |
| CPU inference | Yes |
| Edge/mobile support | No |
| Quantization support | Yes |
| Model formats | ONNX |
| Deployment targets | Windows PCs (x64/ARM64); Windows Server |
| License | Mit |
| Deployment | Desktop, Self-hosted |
| Platforms | Windows |
| Support | Docs |
| Built for | Solo, Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 6 integrations: ONNX Runtime, PyTorch, TensorFlow/Keras, TFLite, scikit-learn, Hugging Face |
| Pricing | Open source |
| Website | learn.microsoft.com |
| Facts checked | 13 Oct 2026 |
Windows ML integrations
Windows ML lists 6 integrations on its own site.
- ONNX Runtime
- PyTorch
- TensorFlow/Keras
- TFLite
- scikit-learn
- Hugging Face
Alternatives to Windows ML
- NVIDIA TensorRTA free SDK for compiling and running optimized deep-learning inference on NVIDIA GPUs.9.4
- vLLMA flexible open-source engine for teams serving LLMs across varied infrastructure.9.3
- Apache TVMA flexible open-source compiler for deploying models across CPUs, GPUs, mobile and edge hardware.9.2
See all Windows ML alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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