AWS r5ad.4xlargevsAWS r5ad.large

r5ad.4xlarge pairs 16 vCPUs with 128GB of RAM at $1.0480/hr On-Demand (about $755/mo at 24×7). r5ad.large pairs 2 vCPUs with 16GB at $0.1310/hr (~$94/mo). r5ad.large is 87% cheaper per hour than r5ad.4xlarge ($0.9170/hr gap).

Because both instances are in the **r5ad family**, the only thing that changes between them is sizing — same silicon, same architecture (AMD EPYC (x86_64)), same burstable/sustained behavior. The choice is purely about how much capacity you actually need: r5ad.4xlarge gives you 16 vCPUs and 128GB of RAM, r5ad.large gives you 2 vCPUs and 16GB. 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 r5ad.large delivers ~694% more single-thread Sysbench score per dollar (1207 vs 9580 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 (10501 vs 1295) are what to weigh. Spot pricing flips many of these comparisons — when r5ad.4xlarge drops to $0.4155/hr and r5ad.large drops to $0.0474/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r5ad.4xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r5ad.large 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.