Two fundamentally different approaches to measuring whether a service is actually working well both fall under “monitoring,” and conflating them means missing what each one is actually good at.
Synthetic monitoring: scripted, controlled, proactive
Synthetic monitoring runs predefined, scripted checks against a service on a regular schedule, from fixed locations, simulating what a user’s interaction would look like without an actual user involved. Because the check is scripted and runs on a fixed schedule from a controlled environment, its results are directly comparable over time — a latency measurement from the same synthetic check run every five minutes from the same location is a clean, consistent signal for trend detection.
Synthetic monitoring’s key structural advantage is that it doesn’t depend on real traffic existing at all: a check can run against a service at 3am when almost no real users are active, catching a problem before it affects the (comparatively few) real users who happen to be online, or even before a new feature has any real traffic at all. This connects directly to the leading-indicator role of saturation in the four golden signals — a synthetic check can catch a developing problem proactively, rather than waiting for it to show up in real usage data.
Real user monitoring: live, variable, representative
Real user monitoring (RUM) takes the opposite approach: instead of a scripted simulation, it measures actual interactions from actual visitors, capturing real variation in device type, network conditions, geographic location, and browser — variation a synthetic script, run consistently from one environment, structurally cannot reproduce. Google’s web.dev documentation on the differences between lab and field data frames this distinction clearly: synthetic monitoring produces “lab” data, collected under controlled, repeatable conditions, while RUM produces “field” data, reflecting what’s actually happening for real users in the real world.
RUM’s advantage is representativeness: it shows what users are genuinely experiencing, including edge cases synthetic checks would never think to script — a specific combination of an older device, a poor mobile connection, and a particular browser version that a lab environment simply doesn’t cover. Its limitation is the mirror image of synthetic monitoring’s strength: RUM data only exists where real traffic already exists, so it can’t catch a problem before real users are already experiencing it, and low-traffic periods or pages produce correspondingly sparse, less statistically reliable data.
Why the two are complementary, not competing
A mature monitoring setup generally uses both, because they answer different questions. Synthetic monitoring answers “is the service working right now, from a consistent, controlled baseline, even with no real traffic” — well suited to alerting and uptime checks feeding into the observability practices covered elsewhere on this site. RUM answers “what are real users, with all their real variation, actually experiencing” — better suited to understanding genuine user-facing performance and prioritizing what to fix based on real-world impact rather than a single controlled scenario.
Key takeaway
Synthetic monitoring’s scripted, controlled checks provide consistent, comparable data and the ability to detect problems proactively, even without real traffic; real user monitoring’s live data captures the genuine variation of actual usage that a synthetic script can’t reproduce, at the cost of only existing where real traffic already does. Neither replaces the other — they’re complementary sources of signal answering different questions about the same system.
This article explains general monitoring concepts; specific tooling and implementation vary by platform. See our disclaimer.