AWS r7gd.16xlargevsAWS r7gd.2xlarge

r7gd.16xlarge pairs 64 vCPUs with 512GB of RAM at $4.3546/hr On-Demand (about $3135/mo at 24×7). r7gd.2xlarge pairs 8 vCPUs with 64GB at $0.5443/hr (~$392/mo). r7gd.2xlarge is 88% cheaper per hour than r7gd.16xlarge ($3.8103/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.16xlarge gives you 64 vCPUs and 512GB of RAM, r7gd.2xlarge gives you 8 vCPUs and 64GB. 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.2xlarge delivers ~700% more single-thread Sysbench score per dollar (693 vs 5545 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 (192414 vs 23911) are what to weigh. Spot pricing flips many of these comparisons — when r7gd.16xlarge drops to $1.2265/hr and r7gd.2xlarge drops to $0.2027/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r7gd.16xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r7gd.2xlarge 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.