Synthetic vs Real User Monitoring

Two Ways to See Performance

Your API responds in 200ms during testing but users complain about slow load times. Your monitoring dashboard shows green status while customers report errors. Uptime checks pass while actual transactions fail. The disconnect happens because most teams monitor only one way—either simulated tests or real user data—when effective performance visibility requires both perspectives.

Synthetic monitoring and real user monitoring (RUM) represent fundamentally different approaches to understanding system performance. Synthetic monitoring actively tests systems with scripted transactions from controlled environments. RUM passively collects performance data from actual users as they interact with your application. Each reveals different truths about how systems behave.

Understanding when to use synthetic monitoring versus RUM, and how to combine them effectively, determines whether your team catches problems proactively or learns about issues from customer complaints.

What Is Synthetic Monitoring

Synthetic monitoring uses automated scripts to simulate user journeys through applications at regular intervals. These scripts execute predefined transactions—logging in, searching products, completing checkouts—from configured locations and measure performance metrics at each step.

Controlled environment testing: Synthetic tests run from known locations using specified browsers, devices, and network conditions. This consistency makes performance changes detectable. When response times increase, you know the system changed, not test conditions.

Proactive problem detection: Synthetic monitoring catches issues before real users encounter them. Tests run continuously, even when no one uses your application. Scheduled checks at 2 AM detect problems before business hours begin.

Pre-production validation: Synthetic monitoring works in staging environments before production deployment. Tests verify that new code performs acceptably before customers see it.

SLA verification: Contractual uptime requirements need objective measurement. Synthetic monitoring provides audit trails showing exactly when services were available from defined locations.

Predictable data collection: Synthetic tests generate metrics consistently. Regular intervals mean dashboards show actual performance trends rather than traffic pattern variations.

Common synthetic monitoring types include uptime checks pinging endpoints every minute, transaction monitoring executing multi-step user flows, and API testing validating service responses.

What Is Real User Monitoring

Real user monitoring captures performance data from actual users interacting with applications in production. Rather than simulating behavior, RUM measures what happens when real people use real devices over real networks.

Passive data collection: RUM doesn’t execute tests. It observes genuine user sessions, recording metrics as browsers load pages, APIs respond to requests, and users navigate applications.

True user experience: Synthetic tests show how systems perform under ideal or controlled conditions. RUM reveals how applications behave when users access them from outdated browsers on slow connections through corporate firewalls.

Geographic distribution: RUM captures performance across all locations users access applications from. You discover regional issues impossible to predict with limited synthetic monitoring locations.

Device diversity: Users employ thousands of device combinations. RUM shows which devices perform poorly, revealing problems synthetic tests from modern browsers miss.

Traffic pattern insights: RUM data correlates with actual usage. Performance issues affecting your largest customer segment appear prominently rather than hidden among synthetic test results.

RUM typically tracks metrics like page load time, time to first byte, time to interactive, JavaScript errors, and navigation timing for every user session.

When Synthetic Monitoring Works Best

Certain monitoring scenarios favor synthetic monitoring’s strengths over RUM.

Baseline availability monitoring: Before worrying about user experience nuances, confirm systems respond to requests. Synthetic uptime checks verify basic availability continuously, alerting teams to outages immediately.

Critical transaction verification: Some user flows matter more than others. Synthetic monitoring tests payment processing, account creation, or checkout flows every few minutes, ensuring critical business functions work.

Third-party dependency tracking: Your application depends on external APIs and services. Synthetic monitoring tests external dependencies directly, attributing failures correctly rather than assuming your code caused problems.

Off-peak monitoring: Real users don’t test applications at 3 AM. Synthetic monitoring does, catching problems during maintenance windows or low-traffic periods when RUM provides no data.

Regression detection: After deployments, synthetic monitoring compares current performance against baselines, immediately detecting performance degradations before significant user traffic arrives.

Service-level agreement compliance: Contractual uptime commitments require objective evidence. Synthetic monitoring from agreed locations provides verifiable SLA data.

Platforms like Upstat implement configurable uptime monitoring that checks service availability at specified intervals, alerting teams when synthetic tests detect downtime or performance degradation before customers are impacted.

When Real User Monitoring Works Best

Different scenarios require RUM’s real-world perspective.

Understanding actual user experience: Synthetic tests show how systems perform under controlled conditions. RUM reveals how real users with diverse devices, browsers, network conditions, and geographic locations actually experience applications.

Identifying performance bottlenecks: Page load times vary by content, caching, and user behavior. RUM identifies which pages, resources, or user flows cause problems for actual users rather than theoretical ones.

Geographic performance analysis: Synthetic monitoring tests from selected locations. RUM captures performance everywhere users access applications, revealing regional CDN issues, routing problems, or infrastructure gaps.

Device and browser compatibility: Synthetic tests run on configured devices. RUM shows which browsers and devices struggle, identifying compatibility issues with older platforms or uncommon configurations.

Long-term trend analysis: RUM data accumulates from continuous real usage, revealing performance trends over weeks and months without manual test scheduling.

Business impact correlation: RUM connects performance to business metrics. Slow checkout pages correlate with abandoned carts. Heavy page load times correlate with reduced engagement. Real user data ties technical performance to business outcomes.

Synthetic vs RUM: Key Differences

The fundamental differences between approaches determine which fits specific needs.

Active vs passive: Synthetic monitoring actively executes tests. RUM passively observes. Active testing finds problems proactively. Passive observation reveals actual experiences.

Controlled vs real-world: Synthetic tests run in controlled environments. RUM captures unpredictable real-world conditions. Control enables comparison. Real-world conditions reveal truth.

Predictive vs reactive: Synthetic monitoring predicts user experience through simulation. RUM reports actual user experience. Prediction catches problems early. Actual data confirms what really happened.

Pre-production vs production: Synthetic monitoring works before production. RUM requires production traffic. Pre-production testing prevents user exposure. Production data confirms real impact.

Cost structure: Synthetic monitoring costs depend on test frequency and locations. RUM costs scale with user traffic volume. Small user bases make synthetic monitoring economical. Large traffic volumes favor RUM.

Data completeness: Synthetic monitoring tests specific scenarios. RUM captures all user interactions. Focused testing verifies critical paths. Comprehensive data reveals unexpected issues.

Neither approach replaces the other. Synthetic monitoring and RUM provide complementary visibility into different aspects of system performance and user experience.

Combining Synthetic and RUM

Most effective monitoring strategies use both approaches together, letting each compensate for the other’s limitations.

Proactive baseline with real-world validation: Synthetic monitoring establishes performance baselines and catches obvious failures. RUM validates that real users experience acceptable performance despite variations synthetic tests cannot simulate.

Pre-production testing and production verification: Synthetic monitoring verifies performance before deployment. RUM confirms real users experience expected performance after release.

Critical path focus and broad coverage: Synthetic monitoring intensively tests critical user flows. RUM broadly monitors all user interactions, catching edge cases synthetic tests miss.

Continuous availability and experience quality: Synthetic monitoring verifies uptime constantly. RUM measures how available systems actually perform for users when available.

Alerting and analysis: Synthetic monitoring triggers alerts for immediate problems requiring action. RUM provides analytical data for identifying trends, patterns, and optimization opportunities.

Practical implementation typically starts with synthetic monitoring for critical paths and uptime verification, then adds RUM for production experience visibility as traffic and budget allow.

Common Pitfalls

Teams implementing monitoring approaches encounter predictable problems.

Over-reliance on synthetic monitoring: Passing synthetic tests don’t guarantee good user experiences. Real-world conditions—network quality, device capabilities, geographic distribution—affect performance in ways synthetic tests cannot capture.

Ignoring synthetic monitoring: RUM shows what real users experience but detects problems only after users encounter them. Without synthetic monitoring, teams learn about outages from customer complaints.

Testing too frequently: Synthetic monitoring every 30 seconds generates noise and costs. Most scenarios need checks every 1-5 minutes, balancing detection speed against overhead.

Testing too infrequently: Checking every 30 minutes means failures could persist 29 minutes before detection. Critical services need frequent synthetic monitoring.

Insufficient test locations: Synthetic monitoring from one region misses geographic failures. Test from locations matching user distribution.

Overwhelming RUM data: Real user monitoring generates enormous data volumes. Without filtering and sampling, signal drowns in noise.

Forgetting maintenance windows: Synthetic alerts during planned maintenance waste time. Implement alert suppression for maintenance periods.

Getting Started

Building effective monitoring doesn’t require implementing everything simultaneously.

Start with synthetic uptime monitoring: Begin with simple HTTP checks verifying critical endpoints respond. This catches outages immediately with minimal effort.

Add critical transaction tests: Identify your most important user flows—login, checkout, data submission—and create synthetic tests executing those paths.

Implement basic alerting: Configure alerts when synthetic tests fail repeatedly, indicating actual problems rather than transient network issues.

Deploy RUM gradually: Add RUM to high-traffic pages first. Analyze collected data before expanding coverage to avoid overwhelming teams with too much information.

Correlate findings: When RUM shows poor performance for specific pages, create targeted synthetic tests verifying those paths. When synthetic tests fail, check RUM data confirming real user impact.

Iterate based on incidents: After each incident, ask whether synthetic monitoring could have caught it earlier and whether RUM data would have revealed the scope faster. Adjust monitoring accordingly.

Start simple, measure results, and expand monitoring based on what actually helps your team respond to problems faster.

Final Thoughts

Synthetic monitoring and real user monitoring provide complementary visibility into system performance and user experience. Synthetic monitoring proactively tests applications under controlled conditions, catching problems before users encounter them. Real user monitoring captures actual user experiences across diverse devices, browsers, and network conditions.

Neither approach replaces the other. Synthetic monitoring’s controlled testing cannot replicate real-world complexity. Real user monitoring’s actual data cannot predict problems before users encounter them. Effective monitoring combines both approaches strategically.

Most teams benefit from starting with synthetic uptime and critical path monitoring, then adding RUM as traffic and resources allow. This progression provides early problem detection through synthetic testing while validating actual user experiences through RUM data.

Choose monitoring approaches based on specific needs. Critical services require frequent synthetic testing. High-traffic applications benefit from comprehensive RUM. Geographic distribution demands multi-location synthetic monitoring. Business-critical transactions need both proactive testing and real-world validation.

Measure monitoring effectiveness by asking whether it helps teams detect and resolve problems faster. Monitoring that generates alerts without enabling action wastes resources. Monitoring that provides visibility enabling faster resolution justifies investment.

Start monitoring critical services today. Simple synthetic uptime checks provide immediate value, and you can expand monitoring sophistication as needs and resources grow.

Monitoring is not overhead—it is the visibility that transforms reactive firefighting into proactive problem prevention and informed performance optimization.