AWS inf2.24xlargevsAWS inf2.xlarge

inf2.24xlarge pairs 96 vCPUs with 384GB of RAM at $6.4906/hr On-Demand (about $4673/mo at 24×7). inf2.xlarge pairs 4 vCPUs with 16GB at $0.7582/hr (~$546/mo). inf2.xlarge is 88% cheaper per hour than inf2.24xlarge ($5.7324/hr gap).

Because both instances are in the **inf2 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: inf2.24xlarge gives you 96 vCPUs and 384GB of RAM, inf2.xlarge gives you 4 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.

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 inf2.24xlarge and 4104/9195 for inf2.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 inf2.24xlarge when your workload is closer to Inferentia ML inference (large-batch ML inference on AWS Inferentia). Pick inf2.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.