AI-powered testing service for device coverage

Yes. There are AI-powered testing platforms that help you choose a smaller, more effective set of devices for testing while maximizing coverage. The key is to distinguish between two capabilities:

• AI-driven device selection: Recommends which devices, OS versions, browsers, and configurations to prioritize based on usage data, risk, and historical failures.
• Coverage optimization: Finds a minimum or near-minimum set of devices that collectively covers the important combinations of hardware, operating system, browser, screen size, and other compatibility factors.

If your goal is to test on more devices without having to run the full test suite on every device, I'd look for a platform that supports both device selection and test execution, rather than one that simply offers a large cloud device farm.

Best services to evaluate

• 

1. Sofy — closest match to your request

(Start here)

Sofy explicitly advertises intelligent test scheduling and automatically selecting a device matrix. It also provides AI-generated test cases, failure analysis, and real-device testing. This is the closest documented match to automatically choosing which devices to test.sofy.ai+3

Best for: mobile apps on iOS and Android.

Visit Sofy

---

• 

2. Applitools — best for visual compatibility coverage

Its Native Mobile Grid can replicate test checkpoints across real and emulated devices using Visual AI. This helps detect layout and rendering defects across screen sizes and OS versions.

The distinction: it focuses on validating across the device matrix, rather than proving the smallest possible subset.applitools.com

Best for: UI regressions and visual differences across devices.

Visit Applitools

---

• 

3. Sauce Labs — best for broad device infrastructure

Offers real devices, virtual devices, thousands of browser/OS combinations, and AI-assisted failure analysis. It's a strong execution platform, although its published capabilities don't establish that it computes a minimum device set.saucelabs.com+1

Best for: teams that need extensive device access and CI automation.

Visit Sauce Labs

---

• 

4. Test Circle — worth investigating

Uses AI to create and execute tests across selected device models and OS versions. It offers broad fleet testing, but I couldn't verify that it automatically minimizes the device matrix for maximum coverage.testcircle.ai

Best for: AI-driven mobile test creation and multi-device execution.

Visit Test Circle

The important distinction: minimum devices vs. maximum coverage

If you want to solve the problem rigorously, ask vendors whether they support risk-based device selection or minimum set-cover optimization.

Imagine your app needs coverage for:

• iOS and Android versions
• Popular and older device models
• Screen sizes and resolutions
• Hardware-specific behaviors
• Browser and OS combinations

A device-selection engine should identify which combination of devices covers the most relevant compatibility risks, while minimizing testing cost and execution time.

The ideal output would look something like this:

These are illustrative numbers, not benchmarks or guaranteed coverage levels. The actual minimum depends on your app and target audience.

One caveat: no finite device set guarantees detection of every possible device-specific defect. The goal is to maximize defined coverage, weighted by real-world usage and risk.

My recommendation

Start by evaluating Sofy for automatic device-matrix selection, and compare it with Applitools if visual compatibility is a major concern. Ask both to demonstrate device selection using your actual device-usage data and test history, rather than a predefined device list.

One question to narrow this down: Are you testing a native iOS/Android app, a mobile web app, or a desktop web app? The best solution depends significantly on that distinction.

What you're looking for is an AI-driven test optimization platform with intelligent device selection — one that identifies the smallest subset of devices needed to achieve full test coverage, rather than running every test on every device.

The key capability is often called test matrix optimization, device farm optimization, or combinatorial test optimization. The right solution depends on whether you're testing mobile phones, tablets, browsers, operating systems, IoT devices, or hardware configurations.

Ideally, the platform should:

• Minimize the device list: Select the fewest devices that collectively cover all required configurations and test scenarios.
• Maintain full coverage: Account for OS versions, device models, screen sizes, hardware capabilities, and other required dimensions.
• Use AI to prioritize testing: Learn from past failures, defect patterns, and usage data to improve test selection.
• Reduce execution cost: Run fewer redundant tests while preserving coverage requirements.
• Adapt over time: Recalculate the optimal device matrix as new devices, OS versions, and test results become available.

One important distinction: pairwise or combinatorial testing can reduce the number of configurations substantially, but it doesn't automatically guarantee full coverage of every requirement. You should look for a platform that explicitly defines and verifies what “full coverage” means for your environment.

Platforms I'd shortlist

These are the closest matches I found, particularly if your device list is for mobile app testing.

• 

1. TestGrid

(Strong starting point)

Focuses on prioritizing relevant device and browser combinations to reduce unnecessary test volume. Supports real-device cloud testing and existing Appium, Selenium, and Cypress scripts.testgrid.io

Best for: reducing a mobile or browser test matrix.

Visit TestGrid

---

• 

2. Perfecto

Combines AI-driven test automation, maintenance, failure analysis, and access to real mobile devices. A strong candidate for enterprise QA teams, although its advertised AI capabilities do not by themselves establish a mathematically minimal device set.www.perforce.com+3

Best for: enterprise-scale testing and test lifecycle automation.

Visit Perfecto

---

• 

3. Applitools

Uses Visual AI to detect UI differences across device and OS combinations and replicate tests across a device matrix. Worth evaluating if visual and layout coverage is important, but it is not specifically a minimum-device-set optimizer.applitools.com+1

Best for: visual regression and cross-device UI consistency.

Visit Applitools

---

• 

4. Marathon Cloud

Uses test history to size the number of virtual devices needed to meet a target execution time. This is particularly relevant if your main goal is reducing the number of devices running in parallel, rather than minimizing the distinct device models needed for coverage.marathonlabs.io+1

Best for: reducing test execution time and parallel device requirements.

Visit Marathon Cloud

One question to narrow this down

When you say “the smallest number of devices required for full coverage,” which kind of optimization do you mean?

1. What are you testing?

Mobile phones and tablets (iOS / Android)

Browsers, operating systems, and screen sizes

Physical devices, IoT, or embedded hardware

Other or mixed environments

---

2. What do you want the platform to minimize?

The fewest distinct device models that still cover all requirements

The fewest device–OS–browser combinations needed for coverage

The fewest devices running concurrently to reduce test time and cost

[Find the best-fit platform]

That distinction matters because selecting the minimum set of devices for coverage is a different optimization problem from simply running tests faster.