AWS g6e.4xlargevsAWS g6e.xlarge

g6e.4xlarge pairs 16 vCPUs with 128GB of RAM at $3.0042/hr On-Demand (about $2163/mo at 24×7). g6e.xlarge pairs 4 vCPUs with 32GB at $1.8610/hr (~$1340/mo). g6e.xlarge is 38% cheaper per hour than g6e.4xlarge ($1.1432/hr gap).

Because both instances are in the **g6e 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: g6e.4xlarge gives you 16 vCPUs and 128GB of RAM, g6e.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.

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 g6e.4xlarge and pending for g6e.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 g6e.4xlarge when your workload is closer to GPU-accelerated (graphics + ML inference) (graphics workloads, video transcoding, ML inference). Pick g6e.xlarge when it's closer to GPU-accelerated (graphics + ML inference) (graphics workloads, video transcoding, ML inference). 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.