AWS r4.largevsAWS r8in.large

r4.large pairs 2 vCPUs with 15.3GB of RAM at $0.1330/hr On-Demand (about $96/mo at 24×7). r8in.large pairs 2 vCPUs with 16GB at $0.2092/hr (~$151/mo). r4.large is 57% cheaper per hour than r8in.large ($0.0762/hr gap).

These are different generations of the same series. **r8in.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. **r4.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 r8in.large delivers ~129% more single-thread Sysbench score per dollar (6985 vs 16004 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 (1395 vs 3529) are what to weigh. Spot pricing flips many of these comparisons — when r4.large drops to $0.0418/hr and r8in.large drops to $0.0554/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r4.large when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r8in.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.