AWS r8gd.4xlargevsAWS r8gd.large

r8gd.4xlarge pairs 16 vCPUs with 128GB of RAM at $1.1757/hr On-Demand (about $846/mo at 24×7). r8gd.large pairs 2 vCPUs with 16GB at $0.1470/hr (~$106/mo). r8gd.large is 87% cheaper per hour than r8gd.4xlarge ($1.0287/hr gap).

Because both instances are in the **r8gd family**, the only thing that changes between them is sizing — same silicon, same architecture (AWS Graviton (ARM64)), same burstable/sustained behavior. The choice is purely about how much capacity you actually need: r8gd.4xlarge gives you 16 vCPUs and 128GB of RAM, r8gd.large gives you 2 vCPUs and 16GB. AWS scales pricing close to linearly within a family, so picking the right size is mostly about right-sizing your workload, not getting a better deal per vCPU.

On raw price-per-performance, the two are r8gd.large delivers ~700% more single-thread Sysbench score per dollar (2837 vs 22700 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 (53167 vs 6491) are what to weigh. Spot pricing flips many of these comparisons — when r8gd.4xlarge drops to $0.3646/hr and r8gd.large drops to $0.0452/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r8gd.4xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r8gd.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.