AWS m5a.largevsAWS m5ad.large

m5a.large pairs 2 vCPUs with 8GB of RAM at $0.0860/hr On-Demand (about $62/mo at 24×7). m5ad.large pairs 2 vCPUs with 8GB at $0.1030/hr (~$74/mo). m5a.large is 20% cheaper per hour than m5ad.large ($0.0170/hr gap).

Both are generation-5 general-purpose instances, but they run on different silicon: **m5a.large** is AMD EPYC (x86_64), **m5ad.large** is AMD EPYC (x86_64). AMD variants (suffix `a`) are typically 10% cheaper than Intel siblings at comparable single-thread performance. Graviton variants (suffix `g`) are usually 20–40% cheaper but require ARM64-compatible binaries — most modern Linux stacks are fine, but verify any compiled extensions, native modules, or third-party binaries before migrating. Same vCPU/RAM ratio, same network performance class, different processor.

On raw price-per-performance, the two are m5a.large delivers ~20% more single-thread Sysbench score per dollar (14616 vs 12194 points per $1/hr). That's the cleanest signal we have for "which one runs your workload faster per dollar," but it only matters if your workload is single-thread-bound; for parallel workloads the multi-core scores (1296 vs 1295) are what to weigh. Spot pricing flips many of these comparisons — when m5a.large drops to $0.0385/hr and m5ad.large drops to $0.0421/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick m5a.large when your workload is closer to general-purpose (balanced general-purpose workloads with a 1:4 vCPU-to-memory ratio). Pick m5ad.large when it's closer to general-purpose (balanced general-purpose workloads with a 1:4 vCPU-to-memory ratio). When neither side is obviously right, the cheaper hourly rate usually wins for fault-tolerant batch workloads, while the higher single-core score usually wins for latency-sensitive web traffic. The regional pricing tables linked from each instance page below show where each is currently cheapest — sometimes a >20% regional gap flips the comparison entirely.