Compare ARM/Ampere compute instances across AWS Graviton, Azure Cobalt, GCP Axion, and OCI Ampere.
Output will appear here...The comparison renders a flat dataset of ARM instance records (provider, family, name, vCPUs, memory, processor, network bandwidth, hourly price, category) grouped and filterable by category and provider; it's a static reference snapshot, not a live pricing API, so treat listed prices as directionally useful for sizing comparisons and re-verify exact current pricing with each provider before finalizing a cost model.
A side-by-side of ARM-based compute across AWS Graviton3 (m7g/c7g/r7g families), Azure's Cobalt-based Dpsv5-class instances, GCP Axion, and OCI Ampere A1, with real per-family vCPU, memory, network bandwidth, and hourly pricing figures rather than marketing comparisons. AWS Graviton3 pricing in the dataset (m7g.large at $0.0816/hr for 2 vCPU/8GB) runs meaningfully below equivalent x86 instance pricing, which is the core reason ARM adoption has grown for stateless, horizontally-scaled workloads where the CPU architecture switch costs little beyond a recompile or multi-arch container build.
The biggest hidden migration cost isn't compute price, it's dependency and CI/CD rework, budget time for auditing native dependencies and multi-arch container build pipeline changes before counting on the sticker-price savings showing up immediately.
ARM instance pricing and family availability both change more frequently than x86 lines right now since all four providers are still actively expanding their ARM catalogs, treat any specific price figure as a snapshot to re-verify at decision time, not a stable reference.
A workload with heavy reliance on architecture-specific performance tuning (hand-optimized SIMD code, for instance) may see meaningfully different real-world performance-per-dollar than the raw vCPU/memory/price comparison suggests, benchmark the actual workload, not just the spec sheet, before committing to a large-scale migration.
For most modern interpreted-language workloads (Node.js, Python, Java, Go) it's frequently just a recompile or a multi-arch container build, since ARM64 support in mainstream runtimes and package ecosystems is mature. The real risk is native dependencies, C extensions, or binary-only third-party libraries without an ARM64 build, those need to be identified and resolved before migration, not discovered mid-deployment.
The general direction (ARM cheaper per vCPU/memory unit than equivalent x86) holds across AWS, Azure, GCP, and OCI, but the exact discount percentage varies by provider and instance family, and shifts over time as providers adjust list pricing. Use the comparison as a starting point for sizing a business case, then confirm current published pricing for your specific target instance sizes before finalizing a migration cost estimate.
No, ARM family coverage varies, some providers have broader ARM coverage across general-purpose, compute-optimized, and memory-optimized families, while GPU-accelerated ARM instances are much rarer across the board. Check the specific family you need against each provider's current ARM catalog rather than assuming parity with their x86 lineup.
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