When to use synthetic monitoring vs. real user monitoring

Before pushing a new landing page, checkout flow or user interface (UI) revamp, synthetic tests are your best friend. They let you benchmark Core Web Vitals and key interactions under clean, reproducible conditions.

You can automate these in CI/CD pipelines or run them against staging builds to catch regressions before users ever see them. Synthetic data keeps the feedback loop tight while code is still moving fast.

Once you deploy, however, you’ll want to start monitoring real user sessions. That’s because real users bring chaos that is unpredictable in the build environment: third-party scripts, ad networks, edge caching, suboptimal networks, etc. Synthetic checks won’t catch these varied conditions.

Rather, you’ll want a RUM tool so you can close the loop by confirming that performance improvements in development actually translate to happier, faster sessions in production.

Synthetic monitors usually run from a handful of global data centers on fast machines. That’s fine for uptime but does not help you predict issues when many of your users are operating midrange Androids over spotty 3G.

RUM, on the other hand, captures what those users actually experience. It’s made to give you insight into the distribution of devices, browsers and networks that make or break perceived performance.

Using a RUM tool is the only way to see, for example, that your checkout script won’t execute on low-end phones in South America while desktop users in the United States are unaffected.

Synthetic pings shine for reliability monitoring. They’re predictable, inexpensive and easy to alert on. If your site goes down at 3 a.m., a synthetic check will know before any human does.

RUM complements this, but it’s not designed for minute-by-minute uptime enforcement. Instead, it tells you who was affected and how badly once an incident occurs. This gives you the context you need to reproduce and resolve the issue, ideally before too many of your end users feel the frustration.

Using them together gives you both a quick indication of an issue, as well as scope into its scale. An outage might technically last three minutes, as an example, but your RUM tool reveals that only 5% of sessions actually failed. This is a nuance you’ll never get from synthetic alone.

Synthetic tools can estimate Largest Contentful Paint (LCP), Cumulative Layout Shift (CLS) and Interaction to Next Paint (INP), but they run in essentially “lab” conditions. Real users, however, introduce chaos that can make or break those metrics.

RUM captures how your Core Web Vitals behave across browsers, devices and network speeds, which is the stuff Google’s CrUX data set is built on. You might find that only 5% of sessions have terrible INP, but that 5% represents thousands of frustrated mobile shoppers. Those are insights synthetic can’t surface.

When a page feels sluggish, it’s rarely just the frontend’s fault. Oftentimes, the issues lie somewhere within backend infrastructure, and it takes a bit of investigation to find that out. Synthetic testing is very helpful here. These tests can confirm that your APIs respond within SLOs (service-level objectives) and that critical flows (like login or checkout) don’t regress.

However, RUM takes it further by showing actual user impact.

With modern distributed tracing (for example, via OpenTelemetry), you can correlate a slow API span with a degraded LCP event inside a real session trace. This lets you directly connect how backend problems are preventing real end users from experiencing your site the way it’s meant to be experienced. You not only see the backend bottleneck, you see exactly how it surfaced in the browser. To learn more about modern approaches to RUM, check out this on-demand webinar by Embrace.

Synthetic monitoring and real user monitoring are complementary tools. Teams that combine both get the best of all worlds: synthetic for controlled baselines and uptime testing, RUM for real-world truth and distributed tracing to connect them.