AWS r6id.2xlargevsAWS r6idn.xlarge

r6id.2xlarge pairs 8 vCPUs with 64GB of RAM at $0.6048/hr On-Demand (about $435/mo at 24×7). r6idn.xlarge pairs 4 vCPUs with 32GB at $0.3908/hr (~$281/mo). r6idn.xlarge is 35% cheaper per hour than r6id.2xlarge ($0.2140/hr gap).

Both are generation-6 memory-optimized instances, but they run on different silicon: **r6id.2xlarge** is Intel Xeon (x86_64), **r6idn.xlarge** 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 r6idn.xlarge delivers ~54% more single-thread Sysbench score per dollar (4927 vs 7569 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 (12370 vs 6260) are what to weigh. Spot pricing flips many of these comparisons — when r6id.2xlarge drops to $0.2016/hr and r6idn.xlarge drops to $0.2050/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r6id.2xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r6idn.xlarge 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.