All three configurations use dedicated physical Apple Silicon nodes, not virtual machines. The key differences are memory, storage, and parallel workload capacity. First estimate how many builds, runners, and experiment processes will run at once, then choose the model and rental term.
Don’t choose by project count alone. An archive, dependency index, simulator, runner, and experiment process may consume resources at the same time. The peak combination is more useful than any single task name.
The table compares only the three models currently available. All prices are in USD. Workload guidance is for selection only and does not guarantee fixed duration or throughput.
| Model | Chip | RAM | SSD | Daily | Weekly | Monthly | Quarterly | Best for |
|---|---|---|---|---|---|---|---|---|
| ArmCLI M4 S | M4 | 16GB | 256GB | $19.3 | $52.1 | $96.4 | $262.2 | Single-project development, feature validation, short-term archiving, and low-concurrency automation |
| ArmCLI M4 L | M4 | 24GB | 512GB | $40.7 | $109.9 | $203.6 | $553.8 | Parallel builds, larger dependency caches, continuous integration, and switching between projects |
| ArmCLI M4 Pro X | M4 Pro | 64GB | 2TB | $61.1 | $165.1 | $305.7 | $831.5 | Multiple runners, higher concurrency, larger working sets, and AI experiments |
Observe one complete build in your current workflow. Record peak memory, working-directory and cache usage, concurrent runners, and the longest queue wait. If peaks are occasional, adjust the rental term first. If sustained peaks keep tasks queued, move to a higher configuration.
“Available” is a suitable starting point; “Recommended” provides more headroom for the workload combination; “Heavy load” means a higher tier should be evaluated first. Actual performance depends on project size, tool versions, and task orchestration.
| Workload | ArmCLI M4 S | ArmCLI M4 L | ArmCLI M4 Pro X | What to monitor |
|---|---|---|---|---|
| Single-project development | Recommended start | Available | Available | Number of IDEs, simulators, and local services running together |
| Parallel builds | Measure peak first | Recommended | More headroom | Parallel jobs, link-stage memory, and queue length |
| Multiple runners | Not the first choice | Limit concurrency | Recommended | Concurrent runners, working-directory isolation, and cache contention |
| Large dependency cache | Watch disk space | Recommended | Good for long-term retention | DerivedData, package caches, archives, and log growth rate |
| AI experiments | Light validation | Medium working set | Evaluate first | Model working set, preprocessing concurrency, peak memory, and intermediate files |
The IDE, indexing, simulator, local services, and browser all use memory together. If swapping or repeated index rebuilds are common, compare the 24GB and 64GB tiers first.
Build speed alone is not the only metric. For continuous integration, also track runner count, queue time, failed retries, and cache hits. Avoid setting concurrency above the workload’s needs.
Packages, DerivedData, archives, test artifacts, and logs keep growing. Define cleanup rules first, then choose the base SSD or added storage for the stable working set.
Short tasks fit daily or weekly terms with limited commitment. Stable development environments, long-running runners, and ongoing experiments are better compared on monthly and quarterly terms. Prices below are list prices and exclude added storage or Thunderbolt 5 linking.
Best for single-project development, compatibility checks, temporary archiving, and low-concurrency automation with clear time limits.
Best for team workflows that retain larger dependency caches, run parallel builds, or keep automation running continuously.
Best for AI experiments with multiple runners, larger caches, higher concurrency, and larger memory working sets.
Migration is not about copying every file at once. Organize the required data, reproduce the toolchain, then connect automation. Each stage has verifiable completion criteria so issues can be isolated quickly.
Migrate only source code, dependency manifests, build scripts, required test data, and sanitized configuration. Handle certificates, keys, environment variables, and business data separately; do not copy local caches indiscriminately to the new node.
Pin versions of Xcode, Swift, the package manager, and automation tools, then restore dependencies and run a clean build. Do not import old DerivedData first, or local state may be mistaken for environment configuration.
Register a self-hosted runner, assign labels that identify its purpose, and limit simultaneous executions. Start the first pipeline with read-only checks, then gradually enable build, test, and artifact-upload steps.
Add-ons address specific capacity or linking needs and should not replace a model upgrade. When memory pressure persists, compare a higher-tier model. Consider added storage first only when the working set is growing while memory remains sufficient.
| Add-on | Daily | Weekly | Monthly | Quarterly | Best for |
|---|---|---|---|---|---|
| +1TB SSD | $2.9 | $7.9 | $14.6 | $39.7 | Dependency caches, archives, and test artifacts exceed base-disk capacity, but more memory is unnecessary |
| +2TB SSD | $5.8 | $15.8 | $29.2 | $79.4 | Long-term retention of larger working sets, multi-version artifacts, or experiment intermediates |
| Thunderbolt 5 linking (per Mac) | $1.1 | $2.9 | $5.3 | $14.4 | Multiple physical nodes are clearly needed and the workflow can validate the benefit of linking |
Inventory DerivedData, package caches, archives, and logs first. If the active working set remains consistently close to base-disk capacity after cleanup, add a 1TB SSD instead of paying long term for data that can be rebuilt automatically.
Suitable for workflows that retain several large projects, test artifacts, or experiment intermediates at once. Business data should still be backed up separately; do not treat the added disk as the only copy.
Thunderbolt 5 linking suits multi-node tasks with clearly defined split boundaries. If the bottleneck is memory in one process or a serial step, adjust the model or workflow before adding nodes.
An upgrade should address reproducible resource pressure, not just the model name. Record the symptoms first, then distinguish memory, disk, concurrency, and term issues.
If the IDE, indexing, simulator, local services, and builds running together cause sustained swapping, process exits, or obvious queueing, reduce unnecessary concurrency and retest first. If the issue is repeatable, compare the 24GB ArmCLI M4 L. If the workload includes multiple runners or a large experiment working set, evaluate the 64GB ArmCLI M4 Pro X directly.
First clean rebuildable caches, old archives, and expired logs, then record stable usage after a complete workload cycle. If memory and concurrency are sufficient but the active working set exceeds base-disk capacity, compare the +1TB SSD or +2TB SSD. If disk growth also comes with sustained memory pressure, upgrading the model usually addresses both limits more effectively.
Not necessarily. Confirm that runners truly execute simultaneously and whether they share large caches or the same working directory. For low concurrency and light tasks, start with the ArmCLI M4 L. When several runners remain parallel, queues need shortening, and peak memory clearly rises, evaluate the ArmCLI M4 Pro X first.
Short-term peaks are better handled by adjusting the rental term first. Rent a higher tier daily or weekly during a release sprint, focused testing, or migration, then return to the stable configuration. Compare monthly or quarterly terms only when the peak becomes a daily workload.
Track the memory working set for models and data preprocessing, intermediate-file size, parallel process count, and experiment duration. Start with a lower configuration for light validation. When the working set, concurrency, or long-term intermediate storage grows, evaluate the ArmCLI M4 Pro X and added storage. Do not infer resource needs from the model name alone.
All three available models can be deployed in Singapore, Japan (Tokyo), South Korea (Seoul), Hong Kong, and the US East. Actual availability is returned in real time by the console when you place an order. Consider network paths, code and data flows, and remote interaction needs together when choosing a node.
If the workload boundary is still unclear, review all three rental plans first. If the model and term are decided, proceed directly to checkout. Every order corresponds to a dedicated physical Mac.