AWS inf1.6xlargevsAWS inf1.xlarge

inf1.6xlarge pairs 24 vCPUs with 48GB of RAM at $1.1800/hr On-Demand (about $850/mo at 24×7). inf1.xlarge pairs 4 vCPUs with 8GB at $0.2280/hr (~$164/mo). inf1.xlarge is 81% cheaper per hour than inf1.6xlarge ($0.9520/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: inf1.6xlarge gives you 24 vCPUs and 48GB 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.

Benchmark data for at least one of these instances is still being collected, so a direct performance-per-dollar comparison isn't possible yet. Sysbench scores are pending for inf1.6xlarge and 1239/3861 for inf1.xlarge. Check back as the benchmark queue completes — newer-generation instances typically score 10–30% higher on single-thread and 15–50% higher on multi-core vs the previous generation in the same series.

In practice, pick inf1.6xlarge 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.