AWS m3.mediumvsAWS m8azn.medium

m3.medium pairs 1 vCPUs with 3.8GB of RAM at $0.0670/hr On-Demand (about $48/mo at 24×7). m8azn.medium pairs 1 vCPUs with 4GB at $0.1032/hr (~$74/mo). m3.medium is 54% cheaper per hour than m8azn.medium ($0.0362/hr gap).

These are different generations of the same series. **m8azn.medium** is the newer generation, and AWS's pattern across generations is fairly consistent: ~10–15% better single-thread, 15–30% better multi-core, and similar or modestly higher per-hour pricing — so the price/performance per dollar usually improves with each generation. **m3.medium** is still available and still works (AWS doesn't retire instance types quickly), but for new workloads the newer generation is typically the better default unless you have a specific reason to pin to the older AMI or there's a meaningful regional pricing advantage today.

On raw price-per-performance, the two are m8azn.medium delivers ~299% more single-thread Sysbench score per dollar (15104 vs 60234 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 (996 vs 6225) are what to weigh. Spot pricing flips many of these comparisons — when m3.medium drops to $0.0186/hr and m8azn.medium drops to $0.0286/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick m3.medium when your workload is closer to general-purpose (balanced general-purpose workloads with a 1:4 vCPU-to-memory ratio). Pick m8azn.medium 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.