AWS m6a.8xlargevsAWS m6a.xlarge

m6a.8xlarge pairs 32 vCPUs with 128GB of RAM at $1.3824/hr On-Demand (about $995/mo at 24×7). m6a.xlarge pairs 4 vCPUs with 16GB at $0.1728/hr (~$124/mo). m6a.xlarge is 87% cheaper per hour than m6a.8xlarge ($1.2096/hr gap).

Because both instances are in the **m6a 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: m6a.8xlarge gives you 32 vCPUs and 128GB of RAM, m6a.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 m6a.xlarge delivers ~700% more single-thread Sysbench score per dollar (2949 vs 23600 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 (72712 vs 9051) are what to weigh. Spot pricing flips many of these comparisons — when m6a.8xlarge drops to $0.6255/hr and m6a.xlarge drops to $0.0796/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick m6a.8xlarge when your workload is closer to general-purpose (balanced general-purpose workloads with a 1:4 vCPU-to-memory ratio). Pick m6a.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.