AWS r7g.largevsAWS r7iz.large

r7g.large pairs 2 vCPUs with 16GB of RAM at $0.1071/hr On-Demand (about $77/mo at 24×7). r7iz.large pairs 2 vCPUs with 16GB at $0.1860/hr (~$134/mo). r7g.large is 74% cheaper per hour than r7iz.large ($0.0789/hr gap).

Both are generation-7 memory-optimized instances, but they run on different silicon: **r7g.large** is AWS Graviton (ARM64), **r7iz.large** is Intel Xeon (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 r7g.large delivers ~56% more single-thread Sysbench score per dollar (28161 vs 18059 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 (5811 vs 3441) are what to weigh. Spot pricing flips many of these comparisons — when r7g.large drops to $0.0421/hr and r7iz.large drops to $0.0509/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r7g.large when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r7iz.large 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.