AWS c3.largevsAWS c4.large

c3.large pairs 2 vCPUs with 3.8GB of RAM at $0.1050/hr On-Demand (about $76/mo at 24×7). c4.large pairs 2 vCPUs with 3.8GB at $0.1000/hr (~$72/mo). c4.large is 5% cheaper per hour than c3.large ($0.0050/hr gap).

These are different generations of the same series. **c4.large** 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. **c3.large** 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 c4.large delivers ~15% more single-thread Sysbench score per dollar (10010 vs 11520 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 (1576 vs 1750) are what to weigh. Spot pricing flips many of these comparisons — when c3.large drops to $0.0355/hr and c4.large drops to $0.0355/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick c3.large when your workload is closer to compute-optimized (CPU-bound work — batch processing, web servers under sustained load, game servers). Pick c4.large when it's closer to compute-optimized (CPU-bound work — batch processing, web servers under sustained load, game servers). 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.