AWS r8g.8xlargevsAWS r8gb.8xlarge

r8g.8xlarge pairs 32 vCPUs with 256GB of RAM at $1.8851/hr On-Demand (about $1357/mo at 24×7). r8gb.8xlarge pairs 32 vCPUs with 256GB at $2.9149/hr (~$2099/mo). r8g.8xlarge is 55% cheaper per hour than r8gb.8xlarge ($1.0298/hr gap).

Both are generation-8 memory-optimized instances, but they run on different silicon: **r8g.8xlarge** is AWS Graviton (ARM64), **r8gb.8xlarge** 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 r8g.8xlarge delivers ~55% more single-thread Sysbench score per dollar (1769 vs 1143 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 (106467 vs 106454) are what to weigh. Spot pricing flips many of these comparisons — when r8g.8xlarge drops to $0.5211/hr and r8gb.8xlarge drops to $0.5048/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r8g.8xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r8gb.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.