AWS r6g.2xlargevsAWS r6g.4xlarge

r6g.2xlarge pairs 8 vCPUs with 64GB of RAM at $0.4032/hr On-Demand (about $290/mo at 24×7). r6g.4xlarge pairs 16 vCPUs with 128GB at $0.8064/hr (~$581/mo). r6g.2xlarge is 100% cheaper per hour than r6g.4xlarge ($0.4032/hr gap).

Because both instances are in the **r6g family**, the only thing that changes between them is sizing — same silicon, same architecture (AWS Graviton (ARM64)), same burstable/sustained behavior. The choice is purely about how much capacity you actually need: r6g.2xlarge gives you 8 vCPUs and 64GB of RAM, r6g.4xlarge gives you 16 vCPUs and 128GB. AWS scales pricing close to linearly within a family, so picking the right size is mostly about right-sizing your workload, not getting a better deal per vCPU.

On raw price-per-performance, the two are r6g.2xlarge delivers ~100% more single-thread Sysbench score per dollar (7071 vs 3535 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 (22807 vs 45612) are what to weigh. Spot pricing flips many of these comparisons — when r6g.2xlarge drops to $0.1178/hr and r6g.4xlarge drops to $0.2416/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r6g.2xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r6g.4xlarge 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.