Synthetic Monitoring

Synthetic monitoring runs predetermined test scripts from known locations and devices at regular intervals, measuring performance against consistent baselines. Unlike real user monitoring that captures actual user data, synthetic tests provide controlled, repeatable measurements that make it easy to detect changes and regressions.

For growth teams, synthetic monitoring serves as an early warning system that catches performance problems before they affect real users. AI enhances synthetic monitoring through intelligent alert tuning that reduces false positives, anomaly detection that identifies subtle performance degradation trends, and automated root cause analysis that pinpoints which component or service is responsible for a slowdown. Growth engineers should deploy synthetic monitors for all critical user flows, running tests from geographic locations that match their user base. The most effective approach combines synthetic and real user monitoring: synthetic tests provide controlled baselines and proactive detection, while RUM data confirms actual user impact and reveals issues that scripted tests cannot replicate. Teams should use synthetic monitoring to validate that deployments do not introduce performance regressions by comparing pre and post-deployment test results.