AWS m2.4xlargevsAWS m5a.4xlarge

m2.4xlarge pairs 8 vCPUs with 68.4GB of RAM at $0.9800/hr On-Demand (about $706/mo at 24×7). m5a.4xlarge pairs 16 vCPUs with 64GB at $0.6880/hr (~$495/mo). m5a.4xlarge is 30% cheaper per hour than m2.4xlarge ($0.2920/hr gap).

These are different generations of the same series. **m5a.4xlarge** 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. **m2.4xlarge** 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 m5a.4xlarge delivers ~75% more single-thread Sysbench score per dollar (1055 vs 1842 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 (6359 vs 10529) are what to weigh. Spot pricing flips many of these comparisons — when m2.4xlarge drops to $0.3491/hr and m5a.4xlarge drops to $0.2856/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick m2.4xlarge when your workload is closer to general-purpose (balanced general-purpose workloads with a 1:4 vCPU-to-memory ratio). Pick m5a.4xlarge 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.