AWS r6a.8xlargevsAWS r6a.xlarge

r6a.8xlarge pairs 32 vCPUs with 256GB of RAM at $1.8144/hr On-Demand (about $1306/mo at 24×7). r6a.xlarge pairs 4 vCPUs with 32GB at $0.2268/hr (~$163/mo). r6a.xlarge is 88% cheaper per hour than r6a.8xlarge ($1.5876/hr gap).

Because both instances are in the **r6a 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: r6a.8xlarge gives you 32 vCPUs and 256GB of RAM, r6a.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 r6a.xlarge delivers ~699% more single-thread Sysbench score per dollar (2248 vs 17972 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 (72642 vs 9057) are what to weigh. Spot pricing flips many of these comparisons — when r6a.8xlarge drops to $0.6329/hr and r6a.xlarge drops to $0.0764/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r6a.8xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r6a.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.