AWS r7a.4xlargevsAWS r7gd.4xlarge

r7a.4xlarge pairs 16 vCPUs with 128GB of RAM at $1.2172/hr On-Demand (about $876/mo at 24×7). r7gd.4xlarge pairs 16 vCPUs with 128GB at $1.0886/hr (~$784/mo). r7gd.4xlarge is 11% cheaper per hour than r7a.4xlarge ($0.1286/hr gap).

Both are generation-7 memory-optimized instances, but they run on different silicon: **r7a.4xlarge** is AMD EPYC (x86_64), **r7gd.4xlarge** 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.4xlarge delivers ~35% more single-thread Sysbench score per dollar (3729 vs 2771 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 (72304 vs 48031) are what to weigh. Spot pricing flips many of these comparisons — when r7a.4xlarge drops to $0.4333/hr and r7gd.4xlarge drops to $0.4535/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r7a.4xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r7gd.4xlarge 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.