AWS r7gd.largevsAWS r8gn.large

r7gd.large pairs 2 vCPUs with 16GB of RAM at $0.1361/hr On-Demand (about $98/mo at 24×7). r8gn.large pairs 2 vCPUs with 16GB at $0.1822/hr (~$131/mo). r7gd.large is 34% cheaper per hour than r8gn.large ($0.0461/hr gap).

These are different generations of the same series. **r8gn.large** 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. **r7gd.large** 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 r7gd.large delivers ~21% more single-thread Sysbench score per dollar (22145 vs 18288 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 (5822 vs 6501) are what to weigh. Spot pricing flips many of these comparisons — when r7gd.large drops to $0.0430/hr and r8gn.large drops to $0.0742/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r7gd.large when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r8gn.large when it's closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). 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.