AWS d3.2xlargevsAWS d3.4xlarge

d3.2xlarge pairs 8 vCPUs with 64GB of RAM at $0.9990/hr On-Demand (about $719/mo at 24×7). d3.4xlarge pairs 16 vCPUs with 128GB at $1.9980/hr (~$1439/mo). d3.2xlarge is 100% cheaper per hour than d3.4xlarge ($0.9990/hr gap).

Because both instances are in the **d3 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: d3.2xlarge gives you 8 vCPUs and 64GB of RAM, d3.4xlarge 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 d3.2xlarge delivers ~100% more single-thread Sysbench score per dollar (1093 vs 547 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 (6676 vs 13455) are what to weigh. Spot pricing flips many of these comparisons — when d3.2xlarge drops to $0.3611/hr and d3.4xlarge drops to $0.7603/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick d3.2xlarge when your workload is closer to storage-optimized (HDD) (dense, sequential HDD storage — HDFS, MapReduce, log warehouses). Pick d3.4xlarge when it's closer to storage-optimized (HDD) (dense, sequential HDD storage — HDFS, MapReduce, log warehouses). 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.