Hand off the workload, not your workflow

Put your
Apple Silicon cloud Mac to work

Nightly builds, automated regression tests, model experiments. Keep using the tools you know and hand execution to a dedicated physical Mac mini—not a virtual machine.

Start with one workload you can validate and run it by the day. Then choose a model and rental term based on memory use, disk growth, and workload frequency.

Rent a cloud Mac Compare configurations & pricing

2 configurations · 5 locations · $20.2per day. Check real-time availability in the console.

Start with a workload you already have

Four workloads, four ways to get started

Treat these recommendations as a starting point, not a performance guarantee. Testing with your own project tells you more than the chip name.

01 / BUILD

React Native nightly builds

Code locally during the day and run iOS builds in the cloud overnight. Start with a fixed branch and verify that Node, CocoaPods, Xcode, and Fastlane versions reproduce your current results.

Start with a single-job queue and keep your dependency lockfile. Store dependency caches separately from final build artifacts. Don’t clear every cache after each build or commit signing materials to your repository.

Check three things:Dependencies install, the build exits successfully, and downstream tests can use the artifacts. Measure cold-cache and repeat-build times separately.

Weekly rental · cover a release cycle

Flow M4
M4 / 16GB / 256GB

Build on Flow M4
02 / RELEASE

Flutter builds for multiple platforms

Build for iOS and macOS in the same macOS environment. Pin Flutter SDK, Dart, and Xcode versions so the same commit uses consistent build settings locally and in the cloud.

Migrate one target platform first, then add others. Simulators, DerivedData, and dependency caches all use disk space; keep the runtimes you need and set cache-cleanup rules.

Check three things:Environment diagnostics pass, the target artifact is produced, and logs and artifacts can be retrieved. If you need to run several builds at once, assess Flow M4 Pro before increasing concurrency.

Monthly rental · keep a consistent dev environment

Flow M4
M4 / 16GB / 256GB

Build on Flow M4
03 / INFERENCE

MLX inference experiments

Test quantized models with the M4 Pro’s 64GB of unified memory. Pin the model version, quantization, context length, and test inputs. Account for runtime and cache usage when checking whether a model fits in memory.

Use a separate Python environment for each experiment. Store model weights, download caches, and experiment results in separate directories. If you have many model files, consider the +2TB SSD option to avoid repeated downloads filling the system disk.

Check three things:The model loads, peak memory use is acceptable, and throughput is reproducible with fixed inputs. Validate by the day before considering a quarterly rental. Extra storage does not increase unified memory.

Start with a daily rental · go longer after validation

Flow M4 Pro
M4 Pro / 64GB / 2TB

Experiment on Flow M4 Pro
04 / TEST

XCUITest regression testing

Run a fixed test suite on a dedicated node to keep regression tests from competing with local development. Pin Xcode, simulator runtimes, and test device configurations. Validate serial runs first, then increase concurrency gradually.

Keep each test result bundle, failure screenshots, and the corresponding commit ID. If tests need a graphical session, verify the session requirements first. SSH access alone doesn’t guarantee that every UI test will run.

Check three things:Results are consistent across repeat runs, failure records are complete, and memory headroom remains under concurrency. Cloud simulator testing does not include physical test devices.

Weekly rental · run a full regression cycle

Flow M4 Pro
M4 Pro / 64GB / 2TB

Run tests on Flow M4 Pro
Validate in parallel before switching over

Three handoffs to move workloads off your local Mac

Validate data, toolchains, and CI separately. If something fails, revisit that step without changing everything at once.

01 · DATA

Move code and caches first

On your local Mac

List your projects, dependency lockfiles, required caches, and artifact directories. Exclude temporary files and obsolete build artifacts, then create a separate backup.

On your cloud Mac

Create a project directory and transfer files incrementally with rsync. For larger projects and caches, consider a +1TB SSD or +2TB SSD based on capacity. Check the actual mount point on the machine.

Set MAC_HOST to your connection address, then run:

rsync -av --progress ./ "$MAC_HOST:~/workspace/"

Handoff complete:Files are readable and writable, directory permissions are correct, and key files pass checksum verification. rsync is not a substitute for a separate backup. On the first migration, don’t enable the option to delete files at the destination.

02 · TOOLCHAIN

Resolve version differences one by one

On your local Mac

Record your Xcode and command-line tools versions, then export your Homebrew dependency list. Identify the certificates and keys the workload needs, and confirm access permissions and scope.

On your cloud Mac

Install and select the required tool versions, then restore dependencies from your list. Import signing materials into the keychain through a controlled process. First verify that command-line tasks can access them in the expected session; never put the keychain password in logs.

Check your current developer tools and paths

xcodebuild -version
xcode-select -p

Handoff complete:The same commit builds with the same parameters. Run the full command once before adding automation; don’t set an unverified environment as the default runner.

03 · CI INTEGRATION

Test with a new label; keep the old path

On your local Mac

Save your current pipeline configuration and list the build, test, archive, and upload steps. Replace hard-coded local paths with a working-directory variable, and note which runner label to restore if you roll back.

On your cloud Mac

Register a self-hosted runner with a dedicated label and least-privilege access. Route a test branch to the new node first, verify artifact uploads and log retrieval, then switch the production pipeline label.

GitHub Actions runner label example

runs-on: [self-hosted, macOS, ARM64, cloud-build]

Handoff complete:Jobs run on the new node, results are traceable, and credentials don’t appear in output. Don’t let untrusted external contributions access a runner that holds signing materials.

Choose for the bottleneck, not the name

Start with memory, then consider storage and collaboration

Start with Flow M4 for lighter builds; consider Flow M4 Pro for parallel builds and MLX. Multi-node collaboration is a separate setup—it doesn’t combine several machines into one large pool of memory.

Workload fit and trial checklist for the two configurations
Workload & bottleneck Recommended starting point Suggested rental term What to check during a trial
Light builds, single-job queue Flow M4
M4 / 16GB / 256GB		 Cover a release cycle with a weekly rental; consider monthly once usage is consistent. Cold- and warm-cache build times, peak memory use, and build directory growth.
Parallel builds, multiple simulator jobs Flow M4 Pro
M4 Pro / 64GB / 2TB		 Validate concurrency by the day, then run a full test suite weekly. Increase the number of jobs gradually and monitor memory pressure, test stability, and disk writes.
MLX quantized model inference Flow M4 Pro
M4 Pro / 64GB / 2TB		 Experiment by the day; choose a monthly or quarterly rental once model fit is confirmed. Total usage from weights, context, and runtime caches, plus throughput with fixed inputs.
Projects, models, or caches use substantial disk space Choose a configuration for your memory needs; also consider a +1TB SSD or +2TB SSD. Check the add-on price for your chosen rental term. Directory layout, mount point, available space, and a complete separate backup.
Work split across multiple nodes Choose a node for each workload, then consider Thunderbolt 5 clustering; each machine is billed separately. Validate software and connectivity requirements before choosing a longer rental. Workload breakdown and data transfer approach. Clustering does not combine unified memory or automatically distribute software workloads.

Choosing a location

Choose from Singapore, Japan (Tokyo), South Korea (Seoul), Hong Kong, and the US East Coast. Both configurations are available at all five locations. For interactive work, choose a location close to the operator; for automated builds, also consider the path for code and artifact transfers.

Tracking your trial

Pin the commit and tool versions, then record setup, build, test, and upload times separately. A fast run alone doesn’t prove a workload is ready for the long term unless you compare the same inputs in the same environment.

Compare rental terms and add-on prices Read the environment & CI setup guide
Make your migration goal concrete

Move less. Validate more.

Define success in one testable sentence: the existing workflow runs, or the experiment confirms compatibility. Decide what to invest in next once you’ve reached that point.

“We moved CI from a local machine to the Seoul node. One change to the pipeline label and it was running.”

React Native engineer

“I rented a machine by the day to try MLX, then switched to a quarterly rental once I knew it was enough.”

AI product prototyper

Choose a workload and try it on a cloud Mac

Bring a project, a set of inputs, and one acceptance criterion. Get the workload running first, then decide how much to move to the cloud.

Rent by the day, week, month, or quarter. Payments are accepted in USDT-TRC20 or Visa, Mastercard, and Amex via Stripe, billed in USD. Check the console for currently available payment gateways.

Rent a cloud Mac Compare the two configurations