AWS r7i.8xlargevsAWS r7iz.12xlarge

r7i.8xlarge pairs 32 vCPUs with 256GB of RAM at $2.1168/hr On-Demand (about $1524/mo at 24×7). r7iz.12xlarge pairs 48 vCPUs with 384GB at $4.4640/hr (~$3214/mo). r7i.8xlarge is 111% cheaper per hour than r7iz.12xlarge ($2.3472/hr gap).

Both are generation-7 memory-optimized instances, but they run on different silicon: **r7i.8xlarge** is Intel Xeon (x86_64), **r7iz.12xlarge** 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 r7i.8xlarge delivers ~105% more single-thread Sysbench score per dollar (1551 vs 755 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 (53856 vs 82934) are what to weigh. Spot pricing flips many of these comparisons — when r7i.8xlarge drops to $0.6350/hr and r7iz.12xlarge drops to $1.2894/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r7i.8xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r7iz.12xlarge 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.