AWS d2.xlargevsAWS d3en.xlarge

d2.xlarge pairs 4 vCPUs with 30.5GB of RAM at $0.6900/hr On-Demand (about $497/mo at 24×7). d3en.xlarge pairs 4 vCPUs with 16GB at $0.5260/hr (~$379/mo). d3en.xlarge is 24% cheaper per hour than d2.xlarge ($0.1640/hr gap).

These are different generations of the same series. **d3en.xlarge** is the newer generation, and AWS's pattern across generations is fairly consistent: ~10–15% better single-thread, 15–30% better multi-core, and similar or modestly higher per-hour pricing — so the price/performance per dollar usually improves with each generation. **d2.xlarge** is still available and still works (AWS doesn't retire instance types quickly), but for new workloads the newer generation is typically the better default unless you have a specific reason to pin to the older AMI or there's a meaningful regional pricing advantage today.

On raw price-per-performance, the two are d3en.xlarge delivers ~22% more single-thread Sysbench score per dollar (1643 vs 2008 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 (3494 vs 3168) are what to weigh. Spot pricing flips many of these comparisons — when d2.xlarge drops to $0.1593/hr and d3en.xlarge drops to $0.0857/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick d2.xlarge when your workload is closer to storage-optimized (HDD) (dense, sequential HDD storage — HDFS, MapReduce, log warehouses). Pick d3en.xlarge 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.