API performance metrics are crucial for measuring the efficiency of a system and ensuring that it can handle a large volume of requests. Three key metrics to consider are Response Time, Throughput, and Latency. Response time measures the time it takes for an API to respond to a request from a client application, while throughput measures the number of requests an API can handle within a given time period. Latency is another important metric that measures the time taken for a client to send a request and get back a response from an API server. Factors affecting these metrics include network latency, database queries, caching mechanisms, and load balancers. To improve these metrics, developers can employ techniques such as optimizing code, implementing caching mechanisms, using horizontal scaling, asynchronous processing, and monitoring API performance with tools like Apache JMeter, Datadog, and New Relic. Additionally, monitoring CPU utilization and memory usage is essential to ensure optimal performance and prevent system instability. By leveraging services like liblab, API providers can generate SDKs for their APIs, further enhancing their developer experience and accelerating the integration process.