AWS r8i.8xlargevsAWS r8i-flex.8xlarge

r8i.8xlarge pairs 32 vCPUs with 256GB of RAM at $2.2227/hr On-Demand (about $1600/mo at 24×7). r8i-flex.8xlarge pairs 32 vCPUs with 256GB at $2.1115/hr (~$1520/mo). r8i-flex.8xlarge is 5% cheaper per hour than r8i.8xlarge ($0.1112/hr gap).

r8i.8xlarge (memory-optimized, Intel Xeon (x86_64)) and r8i-flex.8xlarge (memory-optimized, Intel Xeon (x86_64)) target different workload shapes. The vCPU:RAM ratios are different (32:256 vs 32:256), and depending on the architectures involved you may also see meaningful single-thread performance and per-hour-cost differences. This kind of cross-category comparison is most useful when you're early in the design phase and not yet sure whether your workload is CPU-bound, memory-bound, or balanced — once that's clear, the right pick is usually obvious.

On raw price-per-performance, the two are r8i-flex.8xlarge delivers ~5% more single-thread Sysbench score per dollar (1493 vs 1575 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 (56009 vs 56464) are what to weigh. Spot pricing flips many of these comparisons — when r8i.8xlarge drops to $0.8480/hr and r8i-flex.8xlarge drops to $0.6375/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r8i.8xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r8i-flex.8xlarge 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.