AWS m5.8xlargevsAWS m5.large

m5.8xlarge pairs 32 vCPUs with 128GB of RAM at $1.5360/hr On-Demand (about $1106/mo at 24×7). m5.large pairs 2 vCPUs with 8GB at $0.0960/hr (~$69/mo). m5.large is 94% cheaper per hour than m5.8xlarge ($1.4400/hr gap).

Because both instances are in the **m5 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: m5.8xlarge gives you 32 vCPUs and 128GB of RAM, m5.large gives you 2 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 m5.large delivers ~1393% more single-thread Sysbench score per dollar (711 vs 10615 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 (26931 vs 1503) are what to weigh. Spot pricing flips many of these comparisons — when m5.8xlarge drops to $0.4508/hr and m5.large drops to $0.0363/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick m5.8xlarge when your workload is closer to general-purpose (balanced general-purpose workloads with a 1:4 vCPU-to-memory ratio). Pick m5.large when it's closer to general-purpose (balanced general-purpose workloads with a 1:4 vCPU-to-memory ratio). 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.