AWS m7a.2xlargevsAWS m7a.xlarge

m7a.2xlarge pairs 8 vCPUs with 32GB of RAM at $0.4637/hr On-Demand (about $334/mo at 24×7). m7a.xlarge pairs 4 vCPUs with 16GB at $0.2318/hr (~$167/mo). m7a.xlarge is 50% cheaper per hour than m7a.2xlarge ($0.2318/hr gap).

Because both instances are in the **m7a family**, the only thing that changes between them is sizing — same silicon, same architecture (AMD EPYC (x86_64)), same burstable/sustained behavior. The choice is purely about how much capacity you actually need: m7a.2xlarge gives you 8 vCPUs and 32GB of RAM, m7a.xlarge gives you 4 vCPUs and 16GB. 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 m7a.xlarge delivers ~101% more single-thread Sysbench score per dollar (9207 vs 18552 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 (33202 vs 16481) are what to weigh. Spot pricing flips many of these comparisons — when m7a.2xlarge drops to $0.1997/hr and m7a.xlarge drops to $0.0925/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick m7a.2xlarge when your workload is closer to general-purpose (balanced general-purpose workloads with a 1:4 vCPU-to-memory ratio). Pick m7a.xlarge when it's closer to general-purpose (balanced general-purpose workloads with a 1:4 vCPU-to-memory ratio). 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.