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Drizz raises $2.7M in seed funding •
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Featured on Forbes
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Drizz raises $2.7M in seed funding •
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Featured on Forbes
Your mobile testing tool is not always the answer to your testing problems.
You may be spending money on more devices, but the real costs may be in maintaining your tests. Eventually you'll add devices, buy a new parallel slot, and your test suite grows. Your engineers still spend Fridays fixing automation breakage.
A stack consisting of tools for mobile QA has 3 components that can be discussed independently.
The first component is the test execution infrastructure. Do you run your tests using real devices, emulators, simulators or privately managed test environments in the cloud?
The second component is running a test execution framework. You have options like Appium or alternatively, the framework that is built into the native test automation APIs like Espresso or XCUITest.
The final component is how your test executions adapt to changes to the app UI. Will you perform some amount of manual testing, incorporate self-healing testing, or employ other visual testing APIs? Will you use AI test agents?
If you treat the stack holistically, like most teams do, you will miss the fact that they are actually discrete purchasing decisions.
Compared to BrowserStack and Sauce Labs, AWS Device Farm and Firebase Test Lab offer similar services for different pricing models, scalability, and coverage. Kobiton and Perfecto offer additional custom private solutions.
AI test agents provide a completely different solution that will change how you manage test creation and execution.
The inaccessibility of physical devices is an issue that moving from BrowserStack to AI testing tools will not address. Adding 10,000 more devices will not lessen the burden of maintaining Appium tests if they break with UI changes. Assuming you need devices only occasionally, an enterprise solution with unused hardware might cost more than it is worth.
The first thing to do is determine which layer is costing your team the most.
This article outlines the place of automated tests within an end-to-end mobile CI/CD pipeline by analyzing device clouds, automation frameworks, and AI test agents. It compares tools like BrowserStack, Sauce Labs, AWS Device Farm, Firebase Test Lab, TestMu AI, and Drizz to explain what each replaces and the specific problems they solve.
Reliable mobile testing strategies separate infrastructure from the execution and maintenance of mobile tests.
The infrastructure layer provides devices, the OS, network conditions, session capacity, and concurrent users. BrowserStack, Sauce Labs, AWS Device Farms, Firebase Test Lab, Kobiton, Perfecto, and HeadSpin dominate this layer.
The automation layer allows for the definition of the tests within the mobile application. Appium, Espresso, XCUITest, and Maestro are popular frameworks.
The intelligence and maintenance layer is concerned with how tests behave with UI changes, unanticipated states, and failing tests. Teams develop solutions for this layer through manual updates, locators, visual testing, self-healing systems, and AI-based test agents.
These layers work together, where one does not replace another. Having a large device fleet does not reinforce fragile selectors. Having a scriptless testing tool does not provide all device models or carrier network options. Having a private device lab does not lessen the burden of stable test design.
Mobile tests are best when each test type runs in the appropriate stage.
Before code is merged, perform fast unit tests and a small set of crucial UI checks. These tests should provide feedback in minutes. When dealing with UI checks, use emulators and simulators, and include a small number of real device checks for risky flows.
The goal is quick feedback, not extensive device coverage.
After a merge, perform a broader suite of tests on selected Android and iOS versions. This layer is a good fit for BrowserStack, Sauce Labs, AWS Device Farm, Firebase Test Lab, or another device cloud.
Execute the full regression suite to include more devices, screen sizes, and locales as well as variation in Internet connectivity.
While nightly testing lets teams scan for bugs in more places, it cannot be a team's only means for finding bugs. For example, if a test providing a means for users to log in to the product fails overnight, the team has lost that day to receiving bug feedback.
Each release should be accompanied by a combination of targeted exploratory testing and automated checks on actual devices. Ensure the final build and the release configuration together with the analytics and backend settings are valid for the most important user paths.
A pragmatic pipeline would probably resemble this:
BrowserStack is a good spot for teams looking for a single web and mobile testing platform. Their product suite contains manual and automated testing, real devices and browsers, visual testing with Percy and checks for accessibility, testing and device management, and AI.
The primary advantage is breadth. Teams with large and extensive web and mobile resources tend to prefer a single source, one access, and one reporting platform.
The other side becomes apparent with increased use. Browserstack App Automate pricing is done per parallel test, so more capacity is needed for multiple devices. In the procurement process, device availability during peak periods, target-model coverage, and enterprise pricing should be reviewed.
When you need all of the following from a single vendor, BrowserStack is a great option:
It becomes less favorable when the primary cost is associated with supporting unstable mobile scripts.
TestMu AI is a popular low cost option for teams with working Appium, Selenium, Cypress, or Playwright frameworks. It offers virtual and physical device testing, extensive browser coverage, and parallel execution.
Live and automated real-device testing plans start at $15 and $199 respectively, with prices subject to change. If the cost per parallel run is your primary concern, TestMu AI is a good selection for reliable testing.
Sauce Labs addresses the security and compliance needs of organizations with a focus on enterprise procurement. Real devices, emulators, simulators, Appium support, test analytics, and error reporting are provided along with app distribution.
Its real device cloud, starting at $199 for a single parallel per month when billed annually makes it a strong choice when purchasing is heavily driven by certifications and enterprise controls.
It is not a direct replacement for every BrowserStack feature, and in particular if your team is heavily reliant on the accessibility or visual testing tools.
Testing Bot is designed for small teams with needs for a self-serve plan. It provides options for manual, automated, mobile and browser testing, as well as visual checks.
The comparison lists automated plans that start from $50 per month, while live testing plans start from $20 per user per month. Their device fleet is less comprehensive than BrowserStack’s, so check their coverage for the exact iPhones and Androids your customers use.
AWS Device Farm serves teams that are already in AWS. It provides remote access and automated testing on real iOS and Android devices.
Their model is well suited for irregular testing. The comparison lists prices at around $0.17 per minute per device or $250 per month for an unlimited device slot. Around 1,470 minutes of device time, the unlimited option is less expensive than paying for each minute.
AWS Device Farm is a good choice for bursty workloads. It is less attractive if you need extensive testing outside of provisioned devices.
Firebase Test Lab works well for teams that are focused on Android and require a low-cost entry point. It supports UI Automator, Espresso, XCUITest, and Robo tests.
The free Spark plan has daily usage limits, and other plans have Blaze pricing. While Firebase's Android coverage is highly comprehensive, teams usually require a different provider for robust iOS testing.
Kobiton offers private cloud, on-premises, and hybrid solutions that fit teams with a need for a set of managed devices.
Perfecto targets large enterprises that deal with real devices, network virtualization, scriptless testing, and failure analysis. It is well suited for regulated industries with larger and more elaborate mobile ecosystems.
HeadSpin is dedicated to measuring performance and enhancing user experience. It tests for application usability across various geographical locations and carrier networks, which is highly valuable to companies in the streaming, finance, travel, and other industries in which the state of the network directly impacts the end user’s experience.
AI test agents are more concerned about test maintenance and creation rather than access to devices.
Conventional Appium, Espresso, or XCUITest tests rely on resource IDs, accessibility labels, X-Path Expressions, or other types of selectors. The user journey may still be fully operational, and the tests may still fail due to the team changing a label of a button, restructuring a screen, or otherwise evident changes to a component library.
Self-healing tools address the failure of selectors. AI test agents also go beyond the self-healing tools and any other efforts used to interpret the screen and the intended action.
Drizz works well with automation layers, enabling engineers to build mobile tests using simple and clear English instructions such as "tap login," "tap login 3 times," "tap logout," etc. Drizz’s Vision AI is compatible across iOS and Android and works with real devices, emulators, simulators, runnable locally, and cloud execution.
Drizz does not have to replace your device cloud. Teams can utilize Drizz Cloud or local devices, and even other commercially available device clouds such as BrowserStack. Drizz really shines by reducing selector maintenance and by having a single test suite that can cover both mobile platforms.
Drizz also has support for CI/CD integration, API tests within UI flows, visual regression testing, and zero-trust options for enterprise deployments. The free plan includes 50 test runs, and is followed by pay-per-use and sales-led, Team and Enterprise, plans.
Drizz really shines when the maintenance of tests, as opposed to the availability of devices, is the biggest engineering challenge. If a team has a largely functional Appium suite, or focuses primarily on cross-browser web testing, a device cloud or traditional framework is probably a better fit.
Mobile QA stacks are rarely effective when the goal is to rely on one platform for each requirement. An approach that relies on fast checks with emulators, Sauce Labs for the release of tests, Firebase Test Lab for Android, and Drizz for flow maintenance on both iOS and Android, tends to work better for testing.
The right purchase hinges on the failure you hope to remedy. Infrastructure needs to be improved to remedy a failure caused by unavailable devices. Device coverage needs to be expanded to remedy failure caused by behavioral differences in operating systems. Failure caused by selector breakage needs to be addressed by improving the automation layer.
A device cloud provides a place for tests to run. An automation framework provides instructions. An AI test agent enables instructions to remain effective when the app changes.
The understanding of these constructs leads to better coverage with lower maintenance and a release pipeline that provides feedback to the development team at the right time.
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