Synthetic Monitoring vs Real User Monitoring: Which Does Your Team Need?

If you’ve spent any time evaluating monitoring tools, you’ve likely encountered both synthetic monitoring and real user monitoring (RUM). Vendors often present them as complementary—and they are—but that doesn’t help when you’re trying to decide where to start, what to prioritise, or whether you need both.

This article explains how each approach works, what problems they solve, and where they fail. By the end, you should be able to make a clear decision for your specific situation.

What Is Synthetic Monitoring?

Synthetic monitoring uses scripted, automated probes that simulate user interactions with your service from external locations. You configure a check—typically an HTTP request to a URL—and the monitoring system runs it on a schedule, from one or more geographic locations, and alerts you if the response fails or degrades.

The “synthetic” in the name refers to the fact that the traffic is artificial: it’s generated by a monitoring system, not a real user.

What synthetic monitors typically check

HTTP/HTTPS availability: Does the URL return a 2xx status code?
Response time: How long does it take to respond?
Response content: Does the page contain expected text or elements?
SSL/TLS validity: Is the certificate valid and not about to expire?
DNS resolution: Does the domain resolve correctly?
TCP/UDP connectivity: Is a specific port reachable?
API endpoints: Does the API return the expected response with correct data?

These checks run continuously—typically every 30 seconds to 5 minutes—and from multiple geographic locations. If a check fails from São Paulo but passes from Frankfurt, that tells you something specific: there may be a routing issue, a CDN misconfiguration, or a regional infrastructure problem.

The key advantage: it runs whether or not users are online

Synthetic monitoring catches outages at 3am, when no users are active. It catches problems in geographic regions where you have few users. It runs before you launch a new service and can tell you immediately if a deployment broke something.

This is its defining characteristic. You don’t need traffic to have data.

What Is Real User Monitoring (RUM)?

Real user monitoring instruments your application to collect performance data from actual user sessions. When a real user loads your page or uses your app, a lightweight JavaScript snippet (or mobile SDK) captures timing data—DNS lookup time, TCP connection time, time to first byte, page load time, rendering time—and sends it back to your monitoring platform.

Unlike synthetic monitoring, RUM data reflects what your actual users are experiencing, with their actual browsers, devices, network connections, and geographic locations.

What RUM typically measures

Core Web Vitals: Largest Contentful Paint (LCP), First Input Delay (FID), Cumulative Layout Shift (CLS)—the metrics Google uses in search ranking
Page load time breakdown: DNS, connection, request, response, rendering
Geographic performance: How does load time vary between London and Mumbai?
Browser/device segmentation: Are iPhone 12 users having worse performance than desktop Chrome users?
JavaScript errors: What client-side errors are users actually encountering?
User session data: How does performance correlate with conversion or bounce rate?

The key advantage: it reflects reality

Synthetic checks run from a controlled environment—a clean browser instance on known hardware with a known network connection. Real users have old laptops, throttled mobile connections, browser extensions that interfere with loading, and ad blockers. RUM captures this variance.

A synthetic check might show a 400ms page load time. RUM might show that the 90th percentile of your actual users experiences 2.8 seconds—because many of them are on mobile networks in rural areas.

Where Each Falls Short

Where synthetic monitoring falls short

It misses real user performance variance. A synthetic check from London measures performance for a synthetic probe in London. It tells you nothing about the 15% of your users who are on mobile in rural areas and experiencing 3G speeds.

It won’t catch user-specific bugs. If a specific combination of browser version + operating system + user session state causes a JavaScript error, your synthetic probe won’t experience it.

It can create false confidence. If your synthetic checks all pass, it’s tempting to conclude everything is fine. It might be—or real users might be struggling with something your probes don’t check.

It doesn’t cover all user paths. A synthetic monitor checks the URLs you configure. If a critical checkout flow breaks, but you haven’t scripted a monitor for it, you won’t detect it synthetically.

Where RUM falls short

It requires traffic to generate data. If you have a B2B SaaS with 50 active users who work business hours in one time zone, your RUM data is empty from 6pm to 8am. You won’t know if your service goes down at 2am until morning.

It doesn’t detect downtime cleanly. If your server returns a 500 error, RUM often records nothing—because the JavaScript snippet couldn’t load. RUM measures performance for sessions that successfully initiated; it’s blind to sessions that failed before the page started loading.

The data is noisy. RUM data includes a huge range of conditions—users with full 5G connections, users on hotel WiFi, users with 12 browser tabs open. Extracting meaningful signal requires significant filtering and segmentation.

Setup is more complex. Deploying a synthetic monitor is a five-minute task. Deploying RUM requires instrumenting your application, validating that the data is accurate, configuring dashboards, and deciding which metrics matter.

A Framework for Deciding

Rather than a simple “which is better” answer, it’s more useful to think about what question you’re trying to answer:

“Is my service available?”

→ Synthetic monitoring. RUM can’t tell you if your service is down—only synthetic checks run when no one is there. Start here.

”What is my service’s availability from specific regions?”

→ Synthetic monitoring with multi-location checks. Run checks from the geographic regions where your users are.

”Why is performance slow for some users but not others?”

→ RUM. Synthetic checks won’t surface the variance between user groups. RUM will show you that 90th-percentile mobile users in Asia are experiencing 4-second load times.

”How are my Core Web Vitals performing?”

→ RUM. Google’s Core Web Vitals are based on real user experience, and Google uses field data (real user data, via the Chrome User Experience Report) for search ranking, not lab data. Only RUM measures actual field performance.

”Did a deployment break something?”

→ Synthetic monitoring. Synthetic checks detect availability issues and response time regressions within seconds of a deployment. Set up checks for critical endpoints and watch for anomalies post-deploy.

”Is a specific feature or user flow broken?”

→ Synthetic monitoring with scripted transactions. Multi-step synthetic checks can simulate a login flow, a checkout process, or any user journey you define.

”What are real users actually experiencing?”

→ RUM. Always.

The Right Order for Most Teams

If you’re building your monitoring stack from scratch, here’s a practical sequence:

Step 1: Synthetic monitoring first (always)

Before anything else, set up uptime monitoring for your critical endpoints:

Homepage
API health check endpoint
Login page
Payment or checkout flow (if applicable)
Any endpoint you’d be immediately embarrassed if it went down

Synthetic monitoring is fast to set up, low-cost, and catches the majority of incidents: availability failures, response time regressions, SSL expiry, and DNS problems. For most small and mid-size teams, synthetic monitoring alone catches 80–90% of the incidents that matter.

Step 2: Add RUM when you have enough traffic

Once you’re past a few hundred daily active users, RUM data becomes meaningful. Below that threshold, the sample size is too small to draw conclusions.

Integrate RUM to understand:

Your real-world Core Web Vitals (which affect SEO)
Geographic performance variation
The gap between your synthetic check results and what real users experience

Step 3: Layer in APM for application-layer visibility

Application Performance Monitoring (APM)—distributed traces, database query analysis, service dependency maps—gives you visibility into why something is slow or broken, not just that it’s slow or broken. This is the third layer, not the first.

Synthetic Monitoring vs RUM: Quick Comparison

	Synthetic	RUM
Works without users	✅ Yes	❌ No
Detects downtime	✅ Yes	❌ Partial
Real user performance	❌ No	✅ Yes
Geographic insight	✅ From check locations	✅ From all users
Core Web Vitals (field)	❌ No	✅ Yes
Setup complexity	Low	Medium
Cost	Low	Varies with traffic
False positives	Some	Rare
Catches 3am outages	✅ Yes	❌ No

What Most Monitoring Providers Offer

Synthetic only: StatusApp, UptimeRobot, Better Uptime
RUM only: FullStory (session recording + performance), SpeedCurve, DebugBear
Both: Datadog, New Relic, Dynatrace, Catchpoint, Site24x7

The tools that offer both tend to be the most expensive and the most complex. For many teams, a dedicated synthetic monitoring tool (for uptime and response time) combined with Google Search Console’s Core Web Vitals data (free, field data from Chrome users) gets you 90% of the value at 10% of the cost.

The Bottom Line

Synthetic monitoring and RUM are not competing approaches—they measure different things. Synthetic monitoring tells you if your service is working; RUM tells you how well it’s working for the people actually using it.

For most teams prioritising where to start: synthetic monitoring first. It’s faster to set up, works regardless of traffic volume, and catches the incidents that matter most—outages. Add RUM once you have the traffic to make the data meaningful and the engineering capacity to act on it.

If you can only do one, do synthetic. Knowing your site is down before your users tell you is worth more than granular performance percentiles for the users who managed to load it.