AWS m6g.xlargevsAWS m6gd.large

m6g.xlarge pairs 4 vCPUs with 16GB of RAM at $0.1540/hr On-Demand (about $111/mo at 24×7). m6gd.large pairs 2 vCPUs with 8GB at $0.0904/hr (~$65/mo). m6gd.large is 41% cheaper per hour than m6g.xlarge ($0.0636/hr gap).

Both are generation-6 general-purpose instances, but they run on different silicon: **m6g.xlarge** is AWS Graviton (ARM64), **m6gd.large** is AWS Graviton (ARM64). AMD variants (suffix `a`) are typically 10% cheaper than Intel siblings at comparable single-thread performance. Graviton variants (suffix `g`) are usually 20–40% cheaper but require ARM64-compatible binaries — most modern Linux stacks are fine, but verify any compiled extensions, native modules, or third-party binaries before migrating. Same vCPU/RAM ratio, same network performance class, different processor.

On raw price-per-performance, the two are m6gd.large delivers ~71% more single-thread Sysbench score per dollar (18201 vs 31073 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 (10909 vs 5355) are what to weigh. Spot pricing flips many of these comparisons — when m6g.xlarge drops to $0.0600/hr and m6gd.large drops to $0.0451/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick m6g.xlarge when your workload is closer to general-purpose (balanced general-purpose workloads with a 1:4 vCPU-to-memory ratio). Pick m6gd.large 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.