AWS i4g.xlargevsAWS i4i.xlarge

i4g.xlarge pairs 4 vCPUs with 32GB of RAM at $0.3089/hr On-Demand (about $222/mo at 24×7). i4i.xlarge pairs 4 vCPUs with 32GB at $0.3430/hr (~$247/mo). i4g.xlarge is 11% cheaper per hour than i4i.xlarge ($0.0341/hr gap).

Both are generation-4 storage-optimized (NVMe SSD) instances, but they run on different silicon: **i4g.xlarge** is AWS Graviton (ARM64), **i4i.xlarge** 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 i4g.xlarge delivers ~5% more single-thread Sysbench score per dollar (9101 vs 8673 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 (10930 vs 6155) are what to weigh. Spot pricing flips many of these comparisons — when i4g.xlarge drops to $0.0830/hr and i4i.xlarge drops to $0.1140/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick i4g.xlarge when your workload is closer to storage-optimized (NVMe SSD) (I/O-bound work needing low-latency NVMe local storage — NoSQL, search). Pick i4i.xlarge when it's closer to storage-optimized (NVMe SSD) (I/O-bound work needing low-latency NVMe local storage — NoSQL, search). 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.