AWS Device Farm is a mobile application performance testing application that provides real-time automated testing and reproduction of issues, simulating and testing issues that may occur on a variety of platforms (e.g. iPhone or Samsung mobile device, or multiple operations systems, etc).
$0.01
per instance minute
Pricing
AWS Device Farm
Editions & Modules
Pay as You Go - Desktop Browsers
$0.005
per instance minute
Pay-As-You-Go
$0.17
per device minute
Private Devices
$200.00
per month
Unlimited Testing
$250.00
per month
Offerings
Pricing Offerings
AWS Device Farm
Free Trial
No
Free/Freemium Version
No
Premium Consulting/Integration Services
No
Entry-level Setup Fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
AWS Device Farm
Considered Both Products
AWS Device Farm
Verified User
Executive
Chose AWS Device Farm
We haven't used anything like AWS Device Farm before. I am familiar with Amazon Web Services and when we had our MVP ready to test, we turned to AWS for a solution. AWS Device Farm was exactly what we were looking for as we have a really small team and limit resources.
AWS Device Farm is perfect for small teams who don't have enough resources to conduct large scale testing scenarios or even large teams who would like an easier time with testing on various devices. [AWS] Device Farm makes it very easy to interact with a multitude of devices right from the browser.
We had the enterprise support with AWS, so overall support experience was good with great engineers on the back providing answers. As you may know, overall AWS support is different and this is not different. Responses through the regular web support channel came easily, fast and accurate. We had questions/issues which were solved fast. Documentation is good as well, especially around the test automation pieces.
We haven't used anything like AWS Device Farm before. I am familiar with Amazon Web Services and when we had our MVP ready to test, we turned to AWS for a solution. AWS Device Farm was exactly what we were looking for as we have a really small team and limit resources.