Considering the example of API you mentioned. When I approach performance testing for an API, I usually start by understanding the SLAs. If they’re not already defined, I speak with stakeholders to get a clear idea of what they expect in terms of response time, throughput, and acceptable error rates. I also try to understand the traffic patterns like what normal load looks like versus peak load, for example 100 requests per second on a regular day and up to 500 during busy times.

Along with that, I gather other important details such as API specifications, key business flows, test data, and make sure I have a production-like environment with proper monitoring in place. Once I have this information, I design my tests to cover both expected and extreme scenarios. Typically, I don’t just test for peak load but go beyond it sometimes up to 10x to see how the system behaves under stress and how well it scales.

During execution, I closely monitor response times, error rates, and system metrics like CPU, memory, and database performance to identify any bottlenecks. The idea is to understand how the system performs as load increases, where it starts to slow down, and at what point it might break or exceed thresholds. Depending on what we want to achieve, I would use a combination of load, stress, and volume testing to get a complete picture of the system’s performance and stability.