AWS i4i.xlargevsAWS i8g.xlarge

i4i.xlarge pairs 4 vCPUs with 32GB of RAM at $0.3430/hr On-Demand (about $247/mo at 24×7). i8g.xlarge pairs 4 vCPUs with 32GB at $0.3432/hr (~$247/mo). Both are priced identically per hour on-demand.

These are different generations of the same series. **i8g.xlarge** is the newer generation, and AWS's pattern across generations is fairly consistent: ~10–15% better single-thread, 15–30% better multi-core, and similar or modestly higher per-hour pricing — so the price/performance per dollar usually improves with each generation. **i4i.xlarge** is still available and still works (AWS doesn't retire instance types quickly), but for new workloads the newer generation is typically the better default unless you have a specific reason to pin to the older AMI or there's a meaningful regional pricing advantage today.

On raw price-per-performance, the two are i8g.xlarge delivers ~12% more single-thread Sysbench score per dollar (8673 vs 9714 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 (6155 vs 13182) are what to weigh. Spot pricing flips many of these comparisons — when i4i.xlarge drops to $0.1140/hr and i8g.xlarge drops to $0.1095/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick i4i.xlarge when your workload is closer to storage-optimized (NVMe SSD) (I/O-bound work needing low-latency NVMe local storage — NoSQL, search). Pick i8g.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.