AWS r3.2xlargevsAWS r3.4xlarge

r3.2xlarge pairs 8 vCPUs with 61GB of RAM at $0.6650/hr On-Demand (about $479/mo at 24×7). r3.4xlarge pairs 16 vCPUs with 122GB at $1.3300/hr (~$958/mo). r3.2xlarge is 100% cheaper per hour than r3.4xlarge ($0.6650/hr gap).

Because both instances are in the **r3 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: r3.2xlarge gives you 8 vCPUs and 61GB of RAM, r3.4xlarge gives you 16 vCPUs and 122GB. 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 r3.2xlarge delivers ~101% more single-thread Sysbench score per dollar (1638 vs 816 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 (6652 vs 13410) are what to weigh. Spot pricing flips many of these comparisons — when r3.2xlarge drops to $0.1813/hr and r3.4xlarge drops to $0.3436/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r3.2xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r3.4xlarge 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.