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For a Windows-only application, start with Windows ML’s ONNX Runtime path unless you have a clear reason to control the runtime independently. Windows ML packages and services an ONNX Runtime distribution for Windows, keeps the familiar ONNX Runtime session model, and can obtain hardware-specific execution providers through Microsoft’s supported Windows integration. Standalone ONNX Runtime is the better default when the same application must run on other operating systems or when your team needs complete control over runtime versions, binaries, and update timing.
This is therefore mainly a deployment and lifecycle decision, not a choice between unrelated inference engines. Neither option is universally faster. The correct answer depends on target operating systems, packaging, servicing responsibility, accelerator requirements, and measurements from your actual model and devices.
What you are actually comparing
Windows ML
Windows ML is Microsoft’s Windows-supported distribution and integration layer for ONNX Runtime. Its ONNX Runtime APIs let an application keep the usual model-session patterns while changing how the runtime is installed and, when appropriate, how execution providers are acquired. You can ship the runtime inside the application or use a framework-dependent shared installation serviced through Windows App SDK updates.
The practical benefit is a Windows-oriented support and servicing path. Microsoft manages the Windows ML packaging model, documents the compatible provider catalog, and aligns framework-dependent servicing with Windows App SDK releases. That can reduce the amount of runtime maintenance your application team performs, but it also means accepting Microsoft’s supported release cadence and Windows scope.
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Standalone ONNX Runtime
Standalone ONNX Runtime is the independently distributed runtime. Your team selects the package, runtime version, execution-provider dependencies, and update schedule. It is the natural starting point for applications that also target Linux, macOS, mobile platforms, servers, or embedded systems, or for organizations that must freeze a tested runtime independently of Windows servicing.
Using standalone ONNX Runtime does not prevent a Windows application from using CPU or GPU acceleration. It simply leaves distribution, compatibility testing, and provider updates under your control rather than placing them in Windows ML’s integration and servicing model.
Why the names can be misleading
Windows ML includes ONNX Runtime rather than replacing its programming model with an unrelated inference engine. A developer who already creates ONNX Runtime sessions may be able to preserve most application-level inference code and change package references, initialization, provider selection, and deployment. The meaningful differences are platform coverage, packaging, servicing, and provider acquisition.
Quick decision guide
| Choose | Best fit | Trade-off |
|---|---|---|
| Windows ML with ONNX Runtime APIs | A Windows-only app that benefits from Microsoft-supported servicing, a shared framework-dependent runtime, or dynamically acquired hardware providers. | Framework-dependent deployment requires the Windows App SDK runtime and follows its servicing cadence. Self-contained deployment increases the package and makes your team responsible for updates. |
| Standalone ONNX Runtime | An application that must run on multiple operating systems or needs independent control of runtime versions, binaries, and packaging. | Your team owns runtime and execution-provider distribution, compatibility testing, and security-update timing. |
| DirectML directly | A specialized Windows component needing lower-level DirectX 12-style machine-learning integration, custom scheduling, or a nonstandard engine path. | Integration work is greater. Microsoft says new Windows ONNX Runtime feature development has moved to Windows ML while DirectML remains supported. |
Evaluate both options against the same axes: operating-system coverage, runtime and version control, install size, update ownership, language and packaging support, accelerator availability, startup behavior, and measured performance on the target model and hardware.
Windows ML deployment modes
Self-contained deployment
Self-contained deployment places the Windows ML API, ONNX Runtime engine, and DirectML provider in your application package. Microsoft’s current deployment documentation gives an approximate total of 41 MB for those runtime components. The figure is a documented estimate, not a promise that every application package will have exactly that size; your model, application files, architecture, symbols, and installer compression are additional.
This mode gives the publisher control over when a runtime update ships. You can test a specific combination of model, runtime, provider, and driver assumptions before releasing it, and the application can continue using that tested combination without waiting for a system-wide servicing event. The cost is a larger install and an ongoing obligation to monitor and ship runtime fixes yourself.
Framework-dependent deployment
Framework-dependent deployment uses a shared Windows ML runtime rather than bundling the complete engine in every application. Microsoft identifies it as the smaller-install option and says updates arrive through Windows App SDK servicing. The target machine must already have the required Windows App SDK runtime, so installation and first-run diagnostics must handle a missing or incompatible framework.
This mode is attractive for managed fleets or multiple applications that can share a serviced runtime. It is less suitable when an application must remain pinned to a particular runtime build for long periods, or when the deployment environment cannot guarantee the Windows App SDK prerequisite.
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Microsoft notes that framework-dependent deployment is not supported for C/C++. Choose self-contained deployment or another supported packaging approach for a C++ application rather than assuming the shared-runtime path is available.
| Deployment characteristic | Self-contained | Framework-dependent |
|---|---|---|
| Runtime location | Bundled with the application | Shared Windows ML runtime |
| Documented runtime component size | Approximately 41 MB for Windows ML, ONNX Runtime, and DirectML components | Smaller application install because the runtime is not bundled |
| Update owner | Application publisher | Windows App SDK servicing |
| Prerequisite | No shared Windows ML runtime required | Windows App SDK runtime must be present |
| C/C++ support | Supported deployment choice | Not supported according to Microsoft’s deployment documentation |
Versions, languages, and operating-system prerequisites
The Windows ML 2.x package line ships ONNX Runtime version 1.24 or higher through the Microsoft.Windows.AI.MachineLearning NuGet package, according to Microsoft’s Windows ML version documentation. Treat 1.24 as a floor for that 2.x line, not as a guarantee that every package contains the same exact ONNX Runtime build. Check the version table for the Windows ML release you select, and remember that Microsoft officially supports only the current release.
Microsoft’s documented development requirements include the following:
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- .NET: .NET 8 or later is required for the full Windows ML API surface.
- C++: C++20 or later.
- Python: Python 3.10 through 3.13 on x64 or ARM64.
- CPU architectures: Windows ML supports x64 and ARM64 PCs, subject to the Windows App SDK and package requirements for the selected language.
- Operating system: Use a Windows version supported by the Windows App SDK version selected by your application.
Package names, target frameworks, architecture-specific assets, and installation steps vary by language and deployment mode. Verify those details in the current installation documentation before locking your project file or installer design.
Execution providers and hardware acceleration
CPU and DirectML
Windows ML’s ONNX Runtime distribution includes the CPU provider and the legacy DirectML provider. CPU execution is the broadest compatibility fallback, while DirectML uses supported GPU hardware through the Windows graphics stack. A provider being present in the runtime does not mean that every model operator, data type, or device will execute successfully on it.
NPU and hardware-specific GPU providers
Windows ML can obtain some providers dynamically through its provider catalog. NPU and hardware-optimized GPU providers require more than an installed package: Microsoft specifies Windows 11 version 24H2, build 26100, or later for these hardware-specific paths. The actual device, firmware, graphics or NPU driver, provider package, and model operator coverage also determine availability.
Do not promise that an application will automatically use an NPU or a discrete GPU. At startup, identify the providers that are actually available, select one deliberately, and retain a CPU fallback when your product’s reliability requirements call for it. Log the selected provider and the reason a preferred provider was rejected; otherwise support teams cannot distinguish a missing driver from an unsupported model operation.
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- Confirm the Windows build meets the provider’s requirement.
- Confirm the machine architecture is x64 or ARM64 as required by the package.
- Install and validate the vendor or system driver on each supported device family.
- Check that every model operator and tensor data type used by your production graph is supported by the provider.
- Test memory limits, initialization time, and fallback behavior, not only steady-state inference.
- Record the provider, runtime version, model revision, and driver version in diagnostic logs.
Portability, version control, and servicing trade-offs
When portability dominates
If one codebase must ship on Windows and another desktop or server operating system, standalone ONNX Runtime usually reduces architectural friction. You can select a compatible runtime for each platform while keeping a common model and an application-level abstraction around session creation. Windows ML is a Windows solution; it should not be the dependency that defines a cross-platform core.
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When independent version control dominates
Standalone ONNX Runtime is preferable when certification, reproducibility, or a long-lived product branch requires a specific runtime build. Your release process can pin the engine and provider versions, run regression tests, and update on your own schedule. That control also means you must watch for security fixes, ABI changes, driver interactions, and packaging errors.
When Windows servicing and shared installation dominate
Windows ML framework-dependent deployment is useful when reducing per-application footprint matters and the organization accepts Windows App SDK servicing. It can be a good fit for a fleet of Windows applications maintained by one team, provided the installer verifies the framework prerequisite and the application tolerates a serviced runtime changing outside its own installer release.
How to choose: a practical sequence
- List supported operating systems. If Linux, macOS, mobile, or another non-Windows target is a committed requirement, begin with standalone ONNX Runtime. If the product is Windows-only, continue to the next step.
- Define update ownership. Choose Windows ML framework-dependent deployment when Windows App SDK servicing is acceptable. Choose self-contained Windows ML or standalone ONNX Runtime when the publisher must decide exactly when the engine changes.
- Map accelerator requirements. Write down whether CPU, DirectML GPU, a specific GPU provider, or an NPU is required. Check the Windows build, device families, drivers, and model operator support before selecting a provider.
- Check language and packaging constraints. Confirm the .NET, C++, or Python requirement, architecture, installer technology, and whether framework-dependent deployment is available for that language.
- Prototype the same model on representative hardware. Measure initialization, first inference, warm and cold latency, throughput, memory, and failure behavior with each candidate provider and packaging mode.
- Choose the simplest stack that meets the measurements. Avoid selecting a lower-level API merely because it sounds closer to the hardware. Keep a documented reason for every exception to the default.
Implementation workflow for a Windows application
1. Freeze the model contract
Record the model format, opset, input and output shapes, dynamic dimensions, data types, preprocessing, and postprocessing. Provider selection cannot be evaluated meaningfully if the model changes between tests. Keep the model revision beside the runtime and provider versions in your build metadata.
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For Windows ML, decide between self-contained and framework-dependent packaging before writing installer logic. For standalone ONNX Runtime, select the package and execution-provider binaries for each supported architecture. Keep package selection in a reproducible build rather than downloading provider files at runtime from an unverified location.
3. Implement explicit provider selection
Attempt the preferred provider only after checking that the machine and model satisfy its requirements. If it cannot initialize or cannot execute the graph, report the reason and use the documented fallback policy. A CPU fallback may preserve functionality while exposing a performance warning; silently changing providers can create unpredictable latency.
4. Test clean and upgraded machines
Test a machine with no prerequisite installed, a machine with an older framework, and a machine that receives an in-place runtime update. Also test uninstall and rollback paths. Framework-dependent applications should produce an actionable message when the Windows App SDK runtime is missing instead of a generic model-load error.
5. Instrument production behavior
Capture runtime version, Windows build, architecture, selected execution provider, model identifier, initialization duration, inference duration, and error category. Do not log sensitive input tensors unless your privacy design explicitly permits it. These fields make it possible to identify whether a regression came from a model, provider, runtime servicing event, or driver.
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DirectML is a lower-level API for specialized real-time, high-performance, low-latency, or resource-constrained work. It can also be used indirectly through ONNX Runtime. That lower level may be justified when an application owns a custom DirectX 12 pipeline, needs unusual resource scheduling, or is integrating machine learning into an engine that is not built around ordinary ONNX Runtime sessions.
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For ordinary Windows ONNX deployments, Microsoft’s current guidance points developers toward Windows ML. Microsoft Learn states: “DirectML continues to be supported, but new feature development has moved to Windows ML for Windows-based ONNX Runtime deployments.” This describes Microsoft’s product direction; it does not mean existing DirectML applications stop working.
Do not confuse the Windows ML Runtime API with ONNX Runtime APIs
Newer Windows documentation also describes a separate Windows ML Runtime API. Microsoft labels that API experimental and unsupported for production environments. It is not the same thing as the supported ONNX Runtime APIs included in Windows ML.
If your application needs cross-platform support or already uses cross-platform ONNX Runtime session code, use the ONNX Runtime APIs included in Windows ML rather than migrating to the experimental Windows ML Runtime API solely because its name sounds newer. Treat experimental interfaces as evaluation material, not a production dependency.
How to benchmark fairly
Microsoft does not publish a controlled benchmark proving that Windows ML is faster than standalone ONNX Runtime, or the reverse. Performance varies with hardware configuration and model. A useful comparison therefore has to hold the workload constant:
- Use the identical ONNX model, opset, input shapes, preprocessing, and output handling.
- Run the same Windows build, power mode, driver versions, thread settings, and process architecture.
- Compare equivalent execution providers. Do not compare Windows ML on an NPU with standalone CPU execution and call the result a runtime comparison.
- Separate cold-start time, model load time, provider initialization, first inference, and warmed inference.
- Measure median and tail latency, throughput, peak memory, and sustained behavior over a realistic workload.
- Repeat after installing the chosen packaging mode on a clean machine; package layout can affect startup and provider discovery.
- Test representative low-end, mainstream, and high-end devices rather than extrapolating from one developer workstation.
Use the results to choose a provider and deployment mode, not to make a universal claim about one runtime distribution. Re-run the test when the model, Windows build, provider, driver, or runtime version changes.
Troubleshooting common failures
The application cannot find the framework
This is usually a framework-dependent deployment prerequisite problem. Verify that the required Windows App SDK runtime is installed for the application architecture and that the installed version is compatible with the package. If your environment cannot guarantee that prerequisite, redeploy as self-contained or use standalone ONNX Runtime.
The preferred GPU or NPU provider is unavailable
Check the Windows 11 requirement of version 24H2, build 26100, or later for hardware-specific providers, then verify the device model, architecture, firmware, and driver. Confirm that the provider package is available through the selected Windows ML path and that the model’s operators are supported. Keep CPU or another validated provider as the explicit fallback.
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Provider initialization success does not prove graph compatibility. Inspect unsupported operators, data types, dynamic shapes, and memory requirements. Test the same model on CPU to separate a model problem from provider coverage, then decide whether to export a compatible graph, choose another provider, or retain CPU execution for that workload.
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Performance changed after an update
Record the before-and-after Windows build, runtime, provider, model, and driver versions. Framework-dependent applications can receive Windows App SDK servicing independently of the application release; standalone and self-contained applications can change when your publisher ships an update. Re-run cold-start and warmed benchmarks to identify whether initialization or steady-state inference changed.
A C++ project cannot use framework-dependent deployment
Microsoft documents framework-dependent Windows ML deployment as unsupported for C/C++. Use a supported self-contained Windows ML deployment or select standalone ONNX Runtime, then test the resulting installer and update process on clean machines.
Representative architecture decisions
Windows desktop utility with optional acceleration
A Windows-only utility that must work on varied hardware can use Windows ML’s ONNX Runtime APIs, probe available providers, prefer a validated accelerator, and fall back to CPU. Self-contained packaging is appropriate when the utility must behave identically across unmanaged customer machines and the publisher can ship runtime updates.
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Enterprise suite with several Windows applications
A suite maintained by one organization may benefit from framework-dependent deployment if reducing duplicated runtime files matters and the fleet-management system can install and service the Windows App SDK runtime. The team should test shared-runtime updates against every application in the suite.
Cross-platform product
A product shipping the same inference feature on Windows and Linux should normally use standalone ONNX Runtime behind a platform-neutral abstraction. Windows-specific provider selection can remain an implementation detail, while the model contract and application behavior stay consistent across platforms.
Custom graphics or game engine integration
A component that already manages DirectX 12 resources and needs tightly controlled scheduling may justify direct DirectML integration. That is a specialized engineering choice, not the ordinary answer for an application that simply needs to run an ONNX model on Windows.
Bottom line
Use Windows ML with its ONNX Runtime APIs as the default for a Windows-only application when Microsoft-supported servicing, shared framework deployment, or Windows-managed provider acquisition is valuable. Select self-contained packaging when you need a bundled, publisher-controlled runtime; select framework-dependent packaging when you accept the Windows App SDK prerequisite and servicing cadence.
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Use standalone ONNX Runtime when cross-platform deployment or independent runtime and provider version control is a requirement. Choose DirectML directly only for a specialized lower-level integration. In every case, validate the actual model, provider, Windows build, drivers, packaging mode, and target devices; no official comparison establishes a universal performance winner.
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