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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 device lab costs more than the price of its hardware. It also consumes team time, limits coverage, and affects release speed.
You buy phones, tablets, older Android models, and new iPhones after major OS updates. You add storage, charging equipment, network access, and security controls. Your team then keeps every device updated, connected, unlocked, and ready for automated tests.
The lab looks like a fixed asset until a test requires a device your team does not own. At this point, you open a device cloud and pay for both systems.
Many mobile teams reduce the decision to in-house devices versus a cloud service. The real comparison requires more detail. An in-house lab provides physical access, direct control, and lower marginal costs with high utilization. A device cloud provides broader coverage, flexible capacity, remote access, parallel testing, and less physical maintenance.
Neither option guarantees lower costs.
The true cost comes from getting a reliable test run onto the right device at the right time. For an in-house lab, include hardware purchases, device replacement, OS updates, lab infrastructure, maintenance, engineering time, and utilization. For a device cloud, include device minutes, parallel execution, test storage, artifacts, network access, subscription commitments, and the premium for broad coverage.
Availability also affects release schedules. A lab with 30 devices might offer no useful capacity when 20 tests need the same handset at once. A cloud might provide hundreds of device and OS combinations, although paying for broad access makes little financial sense when your team uses most configurations once a month.
Start the build-or-buy analysis with your testing needs. Measure the number of devices you require, test frequency, parallel execution needs, hardware-specific tests, maintenance capacity, and the release impact of missing coverage.
A hybrid strategy often fits these needs best. Keep high-use devices in-house for development, debugging, and fast feedback. Use a device cloud for wider OS, model, and regression coverage.
This guide provides a practical framework for comparing in-house device labs and device clouds across hardware, infrastructure, maintenance, utilization, parallelism, coverage, security, and engineering time. Use it to calculate the full cost of each model and decide when a hybrid approach fits your team.
To understand your cost implications, you need first to define what coverage you require for your application. Once you do this, list the device types, screen sizes, operating systems, and hardware that your application is used on by your users. See if your equipment inventory has devices like:
Devices should be grouped by:
It is important to classify devices to prevent purchasing too much equipment.
For example, if your team's estimated daily use of 5 physical units, testing use of 15 physical units, and 80 configurations for monthly support indicate varying ownership models.
In-house lab budgets extend beyond purchase price. Consider a three-year ownership period for a more meaningful comparison.
Aside from the cost of each unit, consideration must be given for all cases, cables, chargers, SIMs, mounts, and unit spares. Set a unit replacement policy based on battery health, OS support, and the reliability of your tests.
In-house hardware cost = purchased devices + accessories + replacement devices
A work phone is approximately $800, and if your team replaces it every three years, the annual cost for that unit's hardware is approximately $267.
Devices that are older or more used may even require replacement before the third year because battery failures and poor performance may negatively affect your results.
Infrastructure and security considerations include: charge hubs, power protection, storage, and wired network access.
Additional costs include: device management software, screen lock controls, account controls, physical access controls, and backup internet. Some teams may even need to account for the costs associated with audits, access logs, data wipes, and restrictions placed on production data.
Maintenance and engineering time also account for hidden costs. Engineers and QA testers will, among other things, regularly install OS updates, replace cables, manage developer accounts, reset devices, debug failures, device restores, and run tests.
Figure the average monthly hours for maintenance. Multiply the hours by the loaded hourly rate of the staff for the maintenance cost calculation.
Monthly maintenance hours x loaded hourly rate x 12 = maintenance cost
A device is utilized if your team uses the device. Calculate the ratio of device hours used to device hours available (e.g., based on a standard 40-hour workweek) for development, testing, debugging, and release validation.
Utilization = hours used / hours available
Low utilization becomes a high cost ownership. A device consumed for two hours per week incurs the cost of storage, maintenance, and eventual replacement and updates for the year.
A device cloud shifts the cost of ownership to the cost of using the service and consuming the device. Be mindful of the pricing model because, for many providers, the cost is calculated based on a combination of device minutes, test sessions, concurrency, and subscription tiers.
Estimate your expected monthly automation volume as; Test executions per month × average test duration in minutes × device configuration reruns of tests that fail and repeats of tests across multiple OS versions. A suite requiring 20 minutes per test across 40 configurations would consume 800 device minutes.
Parallel execution would affect both the cost and the speed of delivery. If your team runs 10 sessions simultaneously then a plan that provides two concurrent sessions would mean that the remaining sessions would have to wait in line. The extra waiting time can affect a release, even if the monthly usage appears to be low. Track the peak number of concurrent sessions during:
A higher concurrency tier will cost more, but it may reduce idle engineering time and shorten feedback cycles. Concurrency may affect the cost of storage, artifacts, and network access.
Consider video recordings, screenshots, logs, reports, and test artifacts. Large artifacts will cost more to store and transfer, especially if a team saves the artifacts for an audit or failure analysis. Also, verify the network requirements. Some applications need access to staging environments behind a firewall. Verify whether the cloud service provides private connectivity, allowlists, tunnels, or other access controls.
A monthly or annual plan may lower the per-minute cost, but unused capacity still impacts the total cost. Compare committed use to the previous three to six months of testing. Use the following formula to estimate cloud costs:
Cloud cost = subscription fee + device usage + concurrency + storage + network costs
Keep a different estimate for high-usage months. Release cycles, seasonal changes in traffic, and major updates for operating systems drive variations in testing requests.
The cheapest device minute does not always lead to the lowest testing cost. Include reruns as a result of unavailable devices, poor connections, stale sessions, or unpredictable systems. A better measure is to consider cost per reliable run, which is calculated as follows:
Cost per reliable run = total testing cost ÷ successful completed runs
Track how many sessions were completed without human intervention. Do not trade the upfront savings of a cheaper service for the hidden costs and uncertainty of high failure rates. Consider time costs, too. If you have pull requests waiting for an available device for 2 hours, then labor costs include the time the developer waits, time spent reviewing results, and the context switching the developer is forced to perform.
In-house devices work best for local debugging, development, and workflow needs that require continuous or ongoing interaction with the device. In-house devices also work best for teams that have consistent, high use of devices. A device cloud works best for broad compatibility testing, teams that are distributed, and teams that have a sudden, high need for capacity, as well as teams that need to perform multiple regression tests simultaneously. It also reduces the need to purchase every device and OS combination.
Drizz fits this model and provides mobile teams remote device coverage for automated mobile testing that can be used concurrently for configuration testing, across multiple mobile devices, while the teams do not have to purchase and maintain each mobile device.
Its value is at its strongest when the coverage needed exceeds daily in-house inventory or when testing releases creates spikes in demand. Keep sensitive workflows and specialized hardware on-premises when your security or connectivity requirements do not align with the cloud. Use cloud capacity for combinations of devices with low usage and high coverage value.
A good calculator will evaluate 3 options:
Hybrid cost = Core lab cost + Cloud coverage cost + Integration cost
Take the calculation for 12 and 36 months, a longer period illustrates replacement and maintenance costs. Do normal and peak month releases separately.
Evaluate each quarter. Device coverage alters with your customer base, app features, and operating system mix changes. Evaluate your usage, failed sessions, queue time, and coverage gaps every quarter.
Replace devices with low usage or high failure rate. Add physical devices for workflows that require a lot of hands-on debugging. Broad compatibility checks should be moved to the cloud if the costs of ownership are outweighed by the usage value.
Each test should be linked to a clear business need. In-house devices offer fast feedback with hands-on investigations. Device clouds offer a tradeoff of breadth, concurrency and capacity. A hybrid model combines both when you need daily access for broad releases.
The build-or-buy decision should encompass the total cost of using a device for its intended purpose, not just its purchase price.
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