AWS r5d.16xlargevsAWS r5d.4xlarge

r5d.16xlarge pairs 64 vCPUs with 512GB of RAM at $4.6080/hr On-Demand (about $3318/mo at 24×7). r5d.4xlarge pairs 16 vCPUs with 128GB at $1.1520/hr (~$829/mo). r5d.4xlarge is 75% cheaper per hour than r5d.16xlarge ($3.4560/hr gap).

Because both instances are in the **r5d family**, the only thing that changes between them is sizing — same silicon, same architecture (Intel Xeon (x86_64)), same burstable/sustained behavior. The choice is purely about how much capacity you actually need: r5d.16xlarge gives you 64 vCPUs and 512GB of RAM, r5d.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 r5d.4xlarge delivers ~298% more single-thread Sysbench score per dollar (230 vs 916 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 (51642 vs 12851) are what to weigh. Spot pricing flips many of these comparisons — when r5d.16xlarge drops to $1.3376/hr and r5d.4xlarge drops to $0.5869/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r5d.16xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r5d.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.