AWS c8in.2xlargevsAWS c8ine.2xlarge

c8in.2xlarge pairs 8 vCPUs with 16GB of RAM at $0.5443/hr On-Demand (about $392/mo at 24×7). c8ine.2xlarge pairs 8 vCPUs with 16GB at $0.6804/hr (~$490/mo). c8in.2xlarge is 25% cheaper per hour than c8ine.2xlarge ($0.1361/hr gap).

Both are generation-8 compute-optimized instances, but they run on different silicon: **c8in.2xlarge** is Intel Xeon (x86_64), **c8ine.2xlarge** is Intel Xeon (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 c8in.2xlarge delivers ~24% more single-thread Sysbench score per dollar (6096 vs 4925 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 (14043 vs 14129) are what to weigh. Spot pricing flips many of these comparisons — when c8in.2xlarge drops to $0.2437/hr and the other doesn't, the cheap-per-hour winner can swing meaningfully.

In practice, pick c8in.2xlarge when your workload is closer to compute-optimized (CPU-bound work — batch processing, web servers under sustained load, game servers). Pick c8ine.2xlarge 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.