AWS r5ad.largevsAWS r5ad.xlarge

r5ad.large pairs 2 vCPUs with 16GB of RAM at $0.1310/hr On-Demand (about $94/mo at 24×7). r5ad.xlarge pairs 4 vCPUs with 32GB at $0.2620/hr (~$189/mo). r5ad.large is 100% cheaper per hour than r5ad.xlarge ($0.1310/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.large gives you 2 vCPUs and 16GB of RAM, r5ad.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 r5ad.large delivers ~99% more single-thread Sysbench score per dollar (9580 vs 4824 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 (1295 vs 2612) are what to weigh. Spot pricing flips many of these comparisons — when r5ad.large drops to $0.0474/hr and r5ad.xlarge drops to $0.1133/hr, the cheap-per-hour winner can swing meaningfully.

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