AWS r7gd.2xlargevsAWS r7gd.xlarge

r7gd.2xlarge pairs 8 vCPUs with 64GB of RAM at $0.5443/hr On-Demand (about $392/mo at 24×7). r7gd.xlarge pairs 4 vCPUs with 32GB at $0.2722/hr (~$196/mo). r7gd.xlarge is 50% cheaper per hour than r7gd.2xlarge ($0.2721/hr gap).

Because both instances are in the **r7gd 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: r7gd.2xlarge gives you 8 vCPUs and 64GB of RAM, r7gd.xlarge gives you 4 vCPUs and 32GB. 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 r7gd.xlarge delivers ~100% more single-thread Sysbench score per dollar (5545 vs 11117 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 (23911 vs 12103) are what to weigh. Spot pricing flips many of these comparisons — when r7gd.2xlarge drops to $0.2027/hr and r7gd.xlarge drops to $0.1027/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r7gd.2xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r7gd.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.