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For a Windows-only application, start with Windows ML when Microsoft-supported servicing, optional shared-runtime deployment, or Windows-managed hardware providers are important. Use standalone ONNX Runtime when the same application must run on other operating systems or when your team needs independent control over runtime versions and packaging. Windows ML is not a completely different inference engine: it is Microsoft’s Windows-supported distribution and integration layer for ONNX Runtime. The practical decision is therefore about platform scope, deployment, servicing, provider acquisition, and operational ownership rather than a guaranteed speed difference.
The short decision
| Choose | Best fit | Trade-off |
|---|---|---|
| Windows ML using ONNX Runtime APIs | A Windows-only product that benefits from Microsoft-supported integration, a shared framework-dependent runtime, or providers acquired through Windows ML. | Framework-dependent deployment relies on the Windows App SDK runtime and its servicing cadence. Self-contained deployment increases your package and makes your team responsible for runtime updates. |
| Standalone ONNX Runtime | A product that targets Windows plus Linux, macOS, mobile, containers, or another non-Windows environment, or a team that must pin and update the runtime independently. | Your organization owns distribution of the runtime and the execution-provider dependencies used by the application. |
| DirectML directly | A specialized engine or framework integration requiring lower-level DirectX 12-style machine-learning control. | Integration work is greater. DirectML remains supported, but Microsoft says new Windows-based ONNX Runtime feature development has moved to Windows ML. |
Neither option is universally faster. Microsoft says performance depends on the hardware configuration and the model. A meaningful choice requires measuring the exact model, execution provider, packaging mode, and devices you intend to ship.
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What Windows ML changes—and what it does not
Windows ML includes ONNX Runtime and exposes the familiar ONNX Runtime APIs. Existing code built around model sessions, feeds, outputs, and execution providers can generally retain that programming model while changing how the runtime is installed and, optionally, how providers are obtained. This makes Windows ML primarily a distribution and integration decision, not a requirement to rewrite an inference layer.
With standalone ONNX Runtime, you select a package, choose a version, bundle or install its native binaries, and arrange the execution providers your application needs. With Windows ML, Microsoft supplies a Windows-focused package that can run self-contained with the application or as a framework-dependent shared runtime. The latter can receive updates through Windows App SDK servicing rather than through every application’s installer.
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The distinction matters operationally. A desktop product with no non-Windows edition may value a Microsoft-supported servicing path more than the freedom to carry a completely independent runtime. A library, game engine plug-in, server component, or cross-platform client may need the opposite: one runtime strategy across operating systems and strict control over when a version changes.
Compare the decision criteria before selecting a package
Platform scope
Windows ML is intended for Windows applications. Microsoft documents support on x64 and ARM64 PCs running Windows versions supported by Windows App SDK. Standalone ONNX Runtime is the natural starting point when the same product must also run on Linux, macOS, mobile platforms, or a container image whose base system is not Windows.
A Windows-only codebase can still choose standalone ONNX Runtime, particularly if it is shared with a cross-platform product or if build pipelines already standardize on independently versioned native libraries. Conversely, adopting Windows ML does not prevent you from using ONNX Runtime programming concepts; it changes the Windows distribution path.
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Runtime and version ownership
Standalone ONNX Runtime gives the application team direct control over the version it ships, the time of an upgrade, and the native provider binaries it accepts. That control is useful when reproducibility, long-lived support branches, or synchronized testing with a model conversion pipeline is more important than operating-system servicing.
Windows ML offers a Microsoft-supported Windows distribution. Framework-dependent deployment accepts the Windows App SDK servicing model, while self-contained deployment lets the publisher decide when to update the bundled runtime. Neither model removes the need to test model compatibility after an update; it changes who delivers the runtime bits and when they arrive.
Installation size and update responsibility
Microsoft’s current deployment documentation puts a self-contained Windows ML package containing the Windows ML API, ONNX Runtime engine, and DirectML provider at approximately 41 MB total. This is an approximate figure from Microsoft’s documentation, not a promise that every application’s final installer will have exactly that size; your application, symbols, architecture slices, and other dependencies add to it.
Framework-dependent deployment keeps those runtime components outside the application package and is the smaller-install option. It requires the Windows App SDK runtime to be present on the machine, and servicing is delivered through that shared runtime. Self-contained deployment works when you need a predictable private copy or cannot require a separately installed framework, but your installer and update channel must deliver future runtime fixes.
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Language and packaging constraints
Microsoft documents .NET 8 or later for the full Windows ML API surface, C++20 or later, and Python 3.10 through 3.13 on x64 or ARM64. Exact package names, target frameworks, and deployment requirements vary by language and by deployment mode. Verify the installation instructions for the release you select rather than assuming that a package used by one language can be copied unchanged into another.
One material limitation is that Windows ML framework-dependent deployment is not supported for C/C++. A C++ application that wants Windows ML must account for the supported deployment route, commonly self-contained, and should confirm the current C++ packaging guidance before committing its installer design.
Windows ML deployment modes
Self-contained deployment
Self-contained deployment places the Windows ML API, ONNX Runtime engine, and DirectML provider with the application. It is appropriate when the product must carry a known runtime, when a machine cannot be assumed to have the Windows App SDK runtime, or when release engineering requires application-controlled rollout.
- Choose the Windows ML package and architecture matching the application’s x64 or ARM64 target.
- Include the Windows ML runtime components in the application’s installer or package; Microsoft’s documented aggregate for the core components is approximately 41 MB.
- Verify that the model’s required operators are covered by the selected provider and that the target device has the expected drivers.
- Test installation, upgrade, uninstall, and side-by-side versions on a clean machine, because the application now owns delivery of those runtime files.
- Plan a runtime-update policy. Security, compatibility, and provider fixes do not arrive through a shared Windows App SDK installation unless you ship them.
Framework-dependent deployment
Framework-dependent deployment uses a runtime shared by applications on the system. It reduces the application’s bundled content and receives updates through Windows App SDK servicing, but the Windows App SDK runtime must already be installed or provisioned by your installer or enterprise image.
- Confirm that the target Windows image supports the required Windows App SDK runtime.
- Declare the Windows ML package and the framework-dependent deployment path for the application’s language.
- Add an installation check that reports a missing or incompatible Windows App SDK runtime instead of failing later when an inference session is created.
- Test against the serviced runtime versions your support policy allows; a shared update can affect more than one application on the machine.
- Document that C/C++ is not supported for this deployment mode and use the supported alternative where necessary.
This mode is attractive for managed fleets and products already using Windows App SDK. It is less attractive when a vendor must freeze every native component for a long certification window or cannot control the host’s framework installation.
Version planning for Windows ML 2.x
Microsoft’s Windows ML version documentation says that Windows ML 2.x packages provide ONNX Runtime version 1.24 or higher through the Microsoft.Windows.AI.MachineLearning NuGet package. Treat 1.24 as a floor stated for the 2.x package line, not as a guarantee that every 2.x package contains the identical ONNX Runtime build. Select the specific release from Microsoft’s version table and record that selection in your build manifest.
Microsoft also states that only the current Windows ML release is officially supported. A team maintaining an older product should therefore distinguish between “the application still runs with an older package” and “the package is within Microsoft’s supported servicing window.” Pinning a package for reproducibility is reasonable, but it carries an explicit maintenance cost when the supported release moves on.
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For standalone ONNX Runtime, establish your own compatibility matrix: runtime version, model export version, execution provider version, compiler/toolchain, and supported Windows builds. Keep provider binaries aligned with the runtime rather than updating one component in isolation.
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CPU and DirectML
Windows ML’s ONNX Runtime includes CPU support and the legacy DirectML provider. DirectML can use supported Windows hardware through the DirectX 12 stack, but a model does not automatically gain acceleration merely because a GPU is present. Operator coverage, tensor types, device drivers, memory pressure, and provider ordering all affect the path actually used.
When a model contains an operator that the selected accelerator cannot execute, ONNX Runtime may fall back to another provider or fail, depending on the graph and configuration. Validate the provider assignment and inspect logs during development; do not infer the active device from the machine’s hardware name alone.
NPUs and hardware-specific GPU providers
Windows ML can obtain some hardware-optimized providers dynamically through its provider catalog. Microsoft documents additional conditions for NPU and hardware-specific GPU providers: the PC must run Windows 11 version 24H2, build 26100, or later, and the particular device and driver must expose a compatible provider.
That requirement is a platform floor, not a guarantee of availability. Two computers on the same Windows release can expose different providers because their silicon, firmware, or driver packages differ. A production application should detect available providers at runtime, keep a CPU or other supported fallback when appropriate, and report which provider executed the session so support staff can diagnose differences.
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- Confirm x64 or ARM64 architecture and the exact Windows build.
- Install and test the manufacturer’s production driver, not only a development-machine driver.
- Check that every model operator and data type is supported by the intended provider.
- Measure initialization time, steady-state latency, throughput, and memory use separately.
- Exercise provider fallback and a no-accelerator configuration before release.
- Record the provider actually selected in diagnostic logs, with personally sensitive input data excluded.
Performance: why there is no universal winner
The runtime distribution alone does not determine inference speed. The model architecture, graph optimizations, input shape, quantization, provider, driver, CPU or GPU generation, memory bandwidth, and session options all contribute. Windows ML and standalone ONNX Runtime can use substantially similar ONNX Runtime execution paths, so a headline claim that one is always faster would be misleading.
Benchmark the deployment you will ship, not a developer workstation. Use the same model file, preprocessing, postprocessing, thread settings, provider order, and power mode for both candidates. Warm up the session before timing steady-state inference, then collect enough iterations to separate startup cost from per-request latency.
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A practical benchmark plan
- Choose representative inputs, including the largest and smallest shapes your product accepts.
- Build two release configurations: the Windows ML path and the standalone ONNX Runtime path, with equivalent providers.
- Run on every target class: x64 and ARM64 where supported, CPU-only systems, supported GPUs, and NPU-capable Windows 11 24H2 systems if relevant.
- Measure cold start, first inference, steady-state median and tail latency, throughput, peak memory, and package installation time.
- Repeat after driver updates and runtime updates; acceleration behavior can change without a model change.
- Compare accuracy as well as speed. A faster provider is not acceptable if numerical differences exceed the application’s tolerance.
Use the results to choose a provider and packaging mode per device class. Do not publish a single cross-device percentage as though it applied to every Windows machine; Microsoft’s documentation provides no controlled benchmark proving that Windows ML or standalone ONNX Runtime is universally faster.
DirectML versus Windows ML
DirectML is a lower-level API for specialized real-time, high-performance, low-latency, or resource-constrained integrations. An application can reach DirectML indirectly through ONNX Runtime, which is usually less work than writing a custom DirectX 12 machine-learning path.
Microsoft’s DirectML documentation states: “DirectML continues to be supported, but new feature development has moved to Windows ML for Windows-based ONNX Runtime deployments.” This means existing DirectML applications are not automatically obsolete, but a new ordinary Windows ONNX deployment should evaluate Windows ML first. Choose direct DirectML when your architecture genuinely needs its lower-level control, custom scheduling, or integration with an engine that does not fit the Windows ML abstraction.
Do not confuse the ONNX Runtime APIs included in Windows ML with the separate Windows ML Runtime API described in newer documentation. Microsoft labels that API experimental and unsupported for production environments. Applications needing cross-platform support or existing cross-platform ONNX Runtime session code should use the ONNX Runtime APIs included in Windows ML rather than adopting the experimental API for a production product.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Migration guidance for an existing ONNX Runtime application
When migration is low risk
Migration is usually straightforward when the application already uses standard ONNX Runtime sessions and targets only Windows. The inference code can remain conceptually the same while the project changes package references, native deployment, and provider acquisition. The risk is concentrated in installer behavior, provider availability, and version differences rather than in model invocation logic.
Migration steps
- Inventory the current ONNX Runtime version, execution providers, native files, model formats, and supported CPU architectures.
- Map each provider to its Windows ML equivalent. CPU and legacy DirectML are included; hardware-specific providers may be acquired dynamically and have additional OS and driver requirements.
- Select self-contained or framework-dependent deployment based on who should own updates and whether the host can guarantee the Windows App SDK runtime.
- Replace the standalone runtime package with the appropriate Windows ML package for the selected language and target framework.
- Run model-conformance tests first, then provider-specific tests on clean x64 and ARM64 machines.
- Compare startup, latency, memory, and output accuracy with the benchmark plan above.
- Ship diagnostics that identify the Windows build, runtime package, provider, and model revision without logging sensitive inputs.
Keep a rollback package during rollout. A provider or driver issue can affect only a subset of devices, and an installer that can revert the runtime is easier to support than one that requires replacing the entire application.
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Common failure modes and fixes
The application cannot find a required runtime
This usually indicates that a framework-dependent installation did not provision the Windows App SDK runtime, or that the installed architecture does not match the application. Check the machine’s runtime installation and architecture before changing model code. If the product cannot require a shared runtime, use a supported self-contained deployment.
The expected GPU or NPU provider is missing
Check Windows 11 version 24H2, build 26100 or later for providers that require it, then verify the device model and driver. A compatible Windows build alone is insufficient. Ensure the provider is actually acquired and selected; otherwise ONNX Runtime may use CPU or another fallback.
Session creation fails for one model
Compare the model’s operators, data types, dynamic shapes, and external-data files with the selected provider’s support. Try the CPU provider to separate a model-compatibility problem from an accelerator problem. If CPU succeeds but the accelerator fails, reduce the graph to the unsupported operator or choose a provider with broader coverage.
Performance drops after packaging
Verify that the packaged application is using the intended provider and release configuration. A development machine may have a newer driver, a different power policy, or a provider that is absent on a clean image. Log provider selection and repeat cold-start and steady-state measurements after installation.
A runtime update changes behavior
Framework-dependent applications can receive Windows App SDK servicing independently of an application release. Re-run model-conformance and performance tests against serviced runtimes, and define a support policy for the versions your product accepts. Self-contained products avoid surprise host updates but must ship their own security and compatibility fixes.
Which option fits common application types?
| Application situation | Practical starting point | Reason |
|---|---|---|
| Windows desktop utility distributed through an enterprise image | Windows ML framework-dependent, if the image can guarantee Windows App SDK. | Shared servicing reduces per-application runtime payload and can align with enterprise update management. |
| Windows consumer app with a self-contained installer | Windows ML self-contained. | The installer carries a known runtime and does not depend on a preinstalled framework; the publisher owns updates. |
| Product shipping on Windows and Linux | Standalone ONNX Runtime. | A common cross-platform runtime strategy is more important than Windows-specific servicing. |
| Library embedded by other vendors | Usually standalone ONNX Runtime, unless every host is controlled Windows. | Independent packaging and version control reduce conflicts with a host application’s framework. |
| Custom low-latency DirectX 12 engine | Evaluate direct DirectML. | The lower-level API may provide the control required, despite greater integration work and sustained-engineering status. |
| NPU-focused Windows 11 application | Windows ML with a dynamically acquired compatible provider, subject to validation. | Windows ML is the Microsoft-supported path for newer Windows ONNX Runtime provider development, but hardware and driver checks remain mandatory. |
Release checklist
- State whether the product is Windows-only and whether x64, ARM64, or both are supported.
- Record the exact Windows ML or standalone ONNX Runtime package version.
- Choose self-contained or framework-dependent deployment and assign update ownership.
- Verify language requirements: .NET 8 or later, C++20 or later, or Python 3.10–3.13 as applicable.
- Test CPU, DirectML, and every hardware-specific provider you intend to advertise.
- Validate Windows 11 24H2 build 26100 requirements for NPU and hardware-specific GPU paths.
- Measure the real model on real target devices; do not rely on a runtime-level speed assumption.
- Provide a CPU or other supported fallback where product requirements allow it.
- Log runtime, provider, architecture, Windows build, and model version for support diagnostics.
- Retest after Windows App SDK, driver, provider, or model updates.
Final recommendation
Use Windows ML with its ONNX Runtime APIs as the default for a Windows-only application when Microsoft-supported integration, shared-runtime servicing, or Windows-managed provider acquisition outweighs independent packaging control. Select self-contained deployment when you need a private, predictable runtime; select framework-dependent deployment when a managed Windows App SDK runtime is acceptable.
Use standalone ONNX Runtime when cross-platform deployment, an independently pinned runtime, or a library-friendly distribution model is a first-order requirement. Choose direct DirectML only for an integration that truly needs lower-level control. Whichever route you select, verify provider availability and benchmark the actual model on the devices your customers will use.
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