AWS r5a.4xlargevsAWS r5ad.2xlarge

r5a.4xlarge pairs 16 vCPUs with 128GB of RAM at $0.9040/hr On-Demand (about $651/mo at 24×7). r5ad.2xlarge pairs 8 vCPUs with 64GB at $0.5240/hr (~$377/mo). r5ad.2xlarge is 42% cheaper per hour than r5a.4xlarge ($0.3800/hr gap).

Both are generation-5 memory-optimized instances, but they run on different silicon: **r5a.4xlarge** is AMD EPYC (x86_64), **r5ad.2xlarge** 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 r5ad.2xlarge delivers ~73% more single-thread Sysbench score per dollar (1399 vs 2416 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 (10501 vs 5249) are what to weigh. Spot pricing flips many of these comparisons — when r5a.4xlarge drops to $0.3424/hr and r5ad.2xlarge drops to $0.2398/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r5a.4xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r5ad.2xlarge when it's closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). 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.