AWS c8g.xlargevsAWS c8gd.xlarge

c8g.xlarge pairs 4 vCPUs with 8GB of RAM at $0.1595/hr On-Demand (about $115/mo at 24×7). c8gd.xlarge pairs 4 vCPUs with 8GB at $0.1960/hr (~$141/mo). c8g.xlarge is 23% cheaper per hour than c8gd.xlarge ($0.0364/hr gap).

Both are generation-8 compute-optimized instances, but they run on different silicon: **c8g.xlarge** is AWS Graviton (ARM64), **c8gd.xlarge** 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 c8g.xlarge delivers ~23% more single-thread Sysbench score per dollar (20906 vs 17029 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 (13182 vs 13189) are what to weigh. Spot pricing flips many of these comparisons — when c8g.xlarge drops to $0.0598/hr and c8gd.xlarge drops to $0.0864/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick c8g.xlarge when your workload is closer to compute-optimized (CPU-bound work — batch processing, web servers under sustained load, game servers). Pick c8gd.xlarge when it's closer to compute-optimized (CPU-bound work — batch processing, web servers under sustained load, game servers). 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.