AWS c6g.largevsAWS c8g.large

c6g.large pairs 2 vCPUs with 4GB of RAM at $0.0680/hr On-Demand (about $49/mo at 24×7). c8g.large pairs 2 vCPUs with 4GB at $0.0798/hr (~$57/mo). c6g.large is 17% cheaper per hour than c8g.large ($0.0118/hr gap).

These are different generations of the same series. **c8g.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. **c6g.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 roughly tied on single-thread performance per dollar (41309 vs 41775 Sysbench 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 (5321 vs 6521) are what to weigh. Spot pricing flips many of these comparisons — when c6g.large drops to $0.0218/hr and c8g.large drops to $0.0285/hr, the cheap-per-hour winner can swing meaningfully.

In practice, pick c6g.large when your workload is closer to compute-optimized (CPU-bound work — batch processing, web servers under sustained load, game servers). Pick c8g.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.