AWS r7a.xlargevsAWS r7g.xlarge

r7a.xlarge pairs 4 vCPUs with 32GB of RAM at $0.3043/hr On-Demand (about $219/mo at 24×7). r7g.xlarge pairs 4 vCPUs with 32GB at $0.2142/hr (~$154/mo). r7g.xlarge is 30% cheaper per hour than r7a.xlarge ($0.0901/hr gap).

Both are generation-7 memory-optimized instances, but they run on different silicon: **r7a.xlarge** is AMD EPYC (x86_64), **r7g.xlarge** is AWS Graviton (ARM64). 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 r7a.xlarge delivers ~6% more single-thread Sysbench score per dollar (14923 vs 14085 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 (17851 vs 11824) are what to weigh. Spot pricing flips many of these comparisons — when r7a.xlarge drops to $0.0958/hr and r7g.xlarge drops to $0.0638/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r7a.xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r7g.xlarge 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.