AWS r7gd.8xlargevsAWS r7gd.large

r7gd.8xlarge pairs 32 vCPUs with 256GB of RAM at $2.1773/hr On-Demand (about $1568/mo at 24×7). r7gd.large pairs 2 vCPUs with 16GB at $0.1361/hr (~$98/mo). r7gd.large is 94% cheaper per hour than r7gd.8xlarge ($2.0412/hr gap).

Because both instances are in the **r7gd 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: r7gd.8xlarge gives you 32 vCPUs and 256GB of RAM, r7gd.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 r7gd.large delivers ~1499% more single-thread Sysbench score per dollar (1385 vs 22145 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 (96193 vs 5822) are what to weigh. Spot pricing flips many of these comparisons — when r7gd.8xlarge drops to $0.8929/hr and r7gd.large drops to $0.0425/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r7gd.8xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r7gd.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.