AWS inf1.2xlargevsAWS inf1.xlarge

inf1.2xlarge pairs 8 vCPUs with 16GB of RAM at $0.3620/hr On-Demand (about $261/mo at 24×7). inf1.xlarge pairs 4 vCPUs with 8GB at $0.2280/hr (~$164/mo). inf1.xlarge is 37% cheaper per hour than inf1.2xlarge ($0.1340/hr gap).

Because both instances are in the **inf1 family**, the only thing that changes between them is sizing — same silicon, same architecture (Intel Xeon (x86_64)), same burstable/sustained behavior. The choice is purely about how much capacity you actually need: inf1.2xlarge gives you 8 vCPUs and 16GB of RAM, inf1.xlarge gives you 4 vCPUs and 8GB. 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 inf1.xlarge delivers ~59% more single-thread Sysbench score per dollar (3423 vs 5434 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 (7721 vs 3861) are what to weigh. Spot pricing flips many of these comparisons — when inf1.2xlarge drops to $0.0986/hr and inf1.xlarge drops to $0.0483/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick inf1.2xlarge when your workload is closer to Inferentia ML inference (large-batch ML inference on AWS Inferentia). Pick inf1.xlarge when it's closer to Inferentia ML inference (large-batch ML inference on AWS Inferentia). 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.