AWS c5a.8xlargevsAWS c5ad.8xlarge

c5a.8xlarge pairs 32 vCPUs with 64GB of RAM at $1.2320/hr On-Demand (about $887/mo at 24×7). c5ad.8xlarge pairs 32 vCPUs with 64GB at $1.3760/hr (~$991/mo). c5a.8xlarge is 12% cheaper per hour than c5ad.8xlarge ($0.1440/hr gap).

Both are generation-5 compute-optimized instances, but they run on different silicon: **c5a.8xlarge** is AMD EPYC (x86_64), **c5ad.8xlarge** is AMD EPYC (x86_64). 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 c5a.8xlarge delivers ~12% more single-thread Sysbench score per dollar (1313 vs 1176 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 (27352 vs 27368) are what to weigh. Spot pricing flips many of these comparisons — when c5a.8xlarge drops to $0.5448/hr and c5ad.8xlarge drops to $0.6352/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick c5a.8xlarge when your workload is closer to compute-optimized (CPU-bound work — batch processing, web servers under sustained load, game servers). Pick c5ad.8xlarge 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.