AWS r6g.4xlargevsAWS r6g.xlarge

r6g.4xlarge pairs 16 vCPUs with 128GB of RAM at $0.8064/hr On-Demand (about $581/mo at 24×7). r6g.xlarge pairs 4 vCPUs with 32GB at $0.2016/hr (~$145/mo). r6g.xlarge is 75% cheaper per hour than r6g.4xlarge ($0.6048/hr gap).

Because both instances are in the **r6g 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: r6g.4xlarge gives you 16 vCPUs and 128GB of RAM, r6g.xlarge gives you 4 vCPUs and 32GB. 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 r6g.xlarge delivers ~294% more single-thread Sysbench score per dollar (3535 vs 13919 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 (45612 vs 10896) are what to weigh. Spot pricing flips many of these comparisons — when r6g.4xlarge drops to $0.2416/hr and r6g.xlarge drops to $0.0584/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick r6g.4xlarge when your workload is closer to memory-optimized (memory-bound work — in-memory databases, real-time analytics, large caches). Pick r6g.xlarge 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.