AWS h1.2xlargevsAWS h1.4xlarge

h1.2xlarge pairs 8 vCPUs with 32GB of RAM at $0.4680/hr On-Demand (about $337/mo at 24×7). h1.4xlarge pairs 16 vCPUs with 64GB at $0.9360/hr (~$674/mo). h1.2xlarge is 100% cheaper per hour than h1.4xlarge ($0.4680/hr gap).

Because both instances are in the **h1 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: h1.2xlarge gives you 8 vCPUs and 32GB of RAM, h1.4xlarge gives you 16 vCPUs and 64GB. 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 h1.2xlarge delivers ~58% more single-thread Sysbench score per dollar (4994 vs 3168 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 (11427 vs 24873) are what to weigh. Spot pricing flips many of these comparisons — when h1.2xlarge drops to $0.1902/hr and h1.4xlarge drops to $0.2857/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick h1.2xlarge when your workload is closer to storage-optimized (general-purpose workloads). Pick h1.4xlarge when it's closer to storage-optimized (general-purpose workloads). 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.