AWS r6gd.4xlargevsAWS r6gd.metal

r6gd.4xlarge pairs 16 vCPUs with 128GB of RAM at $0.9216/hr On-Demand (about $664/mo at 24×7). r6gd.metal pairs 64 vCPUs with 512GB at $3.6864/hr (~$2654/mo). r6gd.4xlarge is 300% cheaper per hour than r6gd.metal ($2.7648/hr gap).

Because both instances are in the **r6gd 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: r6gd.4xlarge gives you 16 vCPUs and 128GB of RAM, r6gd.metal gives you 64 vCPUs and 512GB. 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 r6gd.4xlarge delivers ~299% more single-thread Sysbench score per dollar (3045 vs 764 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 (44551 vs 179396) are what to weigh. Spot pricing flips many of these comparisons — when r6gd.4xlarge drops to $0.4185/hr and r6gd.metal drops to $0.9695/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r6gd.4xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r6gd.metal 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.