What is the difference between benchmarking on-premise and cloud databases?

Benchmarking on-premise and cloud databases differs primarily in infrastructure control, scalability options, and environmental variables. On-premise databases run on physical hardware you own and manage, while cloud databases use virtualized resources provided by third-party services like AWS RDS or Azure SQL. This distinction affects how you design tests, measure performance, and account for variables like network latency or shared resources in the cloud.

The first major difference is infrastructure setup and consistency. On-premise benchmarking allows full control over hardware (e.g., CPU, storage type, RAM) and network configuration, making it easier to isolate performance bottlenecks. For example, testing a PostgreSQL instance on a dedicated server with NVMe SSDs lets you measure raw database performance without interference from other workloads. In contrast, cloud databases run in shared environments where resources like disk I/O or CPU might be affected by neighboring tenants. A cloud benchmark might require repeated tests to account for variability, and you’d need to factor in service limits (e.g., AWS’s network bandwidth caps on EC2 instances) that don’t apply on-premise.

Scalability and cost models also impact benchmarking approaches. Cloud databases can scale horizontally (adding read replicas) or vertically (upgrading instance sizes) with minimal effort, but this introduces variables like auto-scaling delays or cold starts. For example, testing an Aurora MySQL cluster’s scaling response time after a sudden load spike is critical for cloud-specific benchmarks. On-premise scaling usually requires physical hardware changes, which are slower but more predictable. Cost adds another layer: cloud benchmarks must consider pay-as-you-go pricing (e.g., data egress fees), while on-premise tests focus on upfront hardware costs and long-term maintenance. A developer benchmarking a cloud database might prioritize optimizing queries to reduce CPU usage and lower costs, whereas on-premise efforts might focus on maximizing hardware utilization.

Finally, network and security constraints differ. On-premise databases often reside in local networks, reducing latency for internal applications. Cloud databases might introduce latency if the application servers aren’t co-located in the same region. For instance, a MongoDB Atlas cluster in us-east-1 will perform differently for a client in Europe versus one in Virginia. Security configurations (e.g., VPNs, encryption overhead) can also skew cloud benchmarks, as traffic traverses public networks. These factors require testers to simulate real-world conditions, such as adding artificial latency in on-premise tests or validating throughput under encrypted cloud connections.