AWS t2.smallvsAWS t4g.small

t2.small pairs 1 vCPUs with 2GB of RAM at $0.0230/hr On-Demand (about $17/mo at 24×7). t4g.small pairs 2 vCPUs with 2GB at $0.0168/hr (~$12/mo). t4g.small is 27% cheaper per hour than t2.small ($0.0062/hr gap).

These are different generations of the same series. **t4g.small** 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. **t2.small** 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 t4g.small delivers ~76% more single-thread Sysbench score per dollar (93783 vs 164881 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 (2079 vs 5157) are what to weigh. Spot pricing flips many of these comparisons — when t2.small drops to $0.0066/hr and t4g.small drops to $0.0067/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick t2.small when your workload is closer to burstable general-purpose (bursty traffic — web apps, dev/test boxes, CI runners, small databases). Pick t4g.small when it's closer to burstable general-purpose (bursty traffic — web apps, dev/test boxes, CI runners, small databases). 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.