AWS d2.2xlargevsAWS d2.4xlarge

d2.2xlarge pairs 8 vCPUs with 61GB of RAM at $1.3800/hr On-Demand (about $994/mo at 24×7). d2.4xlarge pairs 16 vCPUs with 122GB at $2.7600/hr (~$1987/mo). d2.2xlarge is 100% cheaper per hour than d2.4xlarge ($1.3800/hr gap).

Because both instances are in the **d2 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: d2.2xlarge gives you 8 vCPUs and 61GB of RAM, d2.4xlarge 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 d2.2xlarge delivers ~105% more single-thread Sysbench score per dollar (774 vs 377 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 (6576 vs 13007) are what to weigh. Spot pricing flips many of these comparisons — when d2.2xlarge drops to $0.2397/hr and d2.4xlarge drops to $0.5188/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick d2.2xlarge when your workload is closer to storage-optimized (HDD) (dense, sequential HDD storage — HDFS, MapReduce, log warehouses). Pick d2.4xlarge 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.