Both MetalCloud and AWS offer Apple Silicon compute in the cloud, but they serve fundamentally different use cases with dramatically different pricing models. This guide breaks down when to use each platform.
Quick Comparison
| Feature | MetalCloud | AWS EC2 Mac |
|---|---|---|
| Max unified memory | 512GB (M3 Ultra) | 192GB (M2 Ultra) |
| Minimum billing | Per-second | 24 hours |
| Hourly cost (top tier) | £3.50/hr | ~$6.50/hr |
| Primary use case | AI/ML inference, GPU workloads | iOS/macOS CI/CD |
| Setup time | <5 minutes | ~30 minutes |
| Root access | No (sandboxed jobs) | Yes (full instance) |
| AWS integration | No | Full VPC, IAM, etc. |
Pricing Deep Dive
The pricing models couldn't be more different. AWS EC2 Mac requires a 24-hour minimum allocation due to how Dedicated Hosts work with Apple Silicon. Even if you only need 2 hours of compute, you pay for 24.
MetalCloud bills per-second with no minimums. Run a 15-minute inference job? Pay for 15 minutes. This makes MetalCloud dramatically more cost-effective for:
- Development and testing
- Burst inference workloads
- Experimentation with large models
- Any workload under 24 hours
Cost Example: Running Llama 70B for 4 Hours
| Platform | Compute Used | Billed | Cost |
|---|---|---|---|
| MetalCloud M3 Ultra | 4 hours | 4 hours | £14.00 |
| AWS EC2 mac2-m2.metal | 4 hours | 24 hours (minimum) | ~$156.00 |
Hardware Comparison
MetalCloud offers access to M3 Ultra with 512GB unified memory—the latest Apple Silicon with 2.7x the memory of AWS's current M2 Ultra offering. This matters enormously for AI workloads where model size is the primary constraint.
| Spec | MetalCloud M3 Ultra | AWS mac2-m2ultra.metal |
|---|---|---|
| CPU Cores | 32-core | 24-core |
| GPU Cores | 80-core | 76-core |
| Unified Memory | 512GB | 192GB |
| Memory Bandwidth | 800GB/s | 800GB/s |
| Neural Engine | 32-core | 32-core |
Use Case Fit
Choose MetalCloud When:
- Running large language models — 512GB handles Llama 70B+ at full precision
- Burst or irregular workloads — Per-second billing saves money
- Quick experimentation — Spin up in minutes, no 24-hour commitment
- MLX framework development — Optimized for Apple's ML framework
- Cost-sensitive projects — Up to 10x cheaper for short workloads
Choose AWS EC2 Mac When:
- iOS/macOS CI/CD pipelines — AWS's primary use case
- Full instance access needed — Root access, custom OS configuration
- AWS ecosystem integration — VPC, IAM, CloudWatch, etc.
- 24/7 dedicated compute — 24-hour minimum becomes irrelevant
- Enterprise compliance requirements — AWS certifications matter
The Verdict
For AI/ML workloads, MetalCloud wins on both capability (512GB vs 192GB) and cost (per-second vs 24-hour minimum). For CI/CD and DevOps requiring full instance access and AWS integration, EC2 Mac remains the better choice. They're complementary services for different needs.
Migration Considerations
If you're currently using AWS EC2 Mac for AI workloads and considering MetalCloud:
- Code changes: Minimal. MetalCloud's Python SDK works with existing MLX/PyTorch code
- Data transfer: Upload models via our SDK or pre-configure popular models
- Workflow changes: Submit jobs via API instead of SSHing to instances
- Cost savings: Typically 5-10x for workloads under 8 hours