Understanding Synthetic Monitoring
Synthetic monitoring, sometimes called proactive or active monitoring, runs scripted checks against a system on a fixed schedule, simulating what a real user would do: loading a homepage, logging in, adding an item to a cart, or calling a specific API endpoint. Unlike real user monitoring, which only captures data when actual traffic flows through the system, synthetic monitoring generates its own traffic continuously, from multiple geographic locations, at a predictable cadence, typically every one to five minutes. This makes it especially valuable for catching problems during low-traffic windows, such as 3 a.m., when real user monitoring might not surface an outage for hours simply because nobody happens to be using the affected feature.
How It Works
Teams define synthetic checks ranging from simple uptime pings (does this URL return a 200 status) to complex multi-step transaction scripts (log in, search for a product, add to cart, complete checkout with a test payment method) run through tools like Datadog Synthetics, Pingdom, Grafana Synthetic Monitoring, or New Relic Synthetics. These checks execute from multiple points of presence around the world, which is useful for detecting regional outages, CDN issues, or DNS problems that only affect users in specific geographies. Results feed directly into alerting: if a synthetic check fails from three consecutive locations, or fails for two consecutive runs, it pages the on-call engineer just like any other production alert.
A Concrete Example
A SaaS company runs a synthetic check every two minutes that logs into their application with a dedicated test account and verifies the dashboard loads within 3 seconds. At 4 a.m., a certificate renewal job fails silently, causing the login page to serve an expired TLS certificate. Because real user traffic overnight is minimal, almost no actual customers hit the broken login page during the first hour. The synthetic check, however, fails on its very next run, triggers a page after two consecutive failures, and the on-call engineer fixes the certificate before the morning traffic wave arrives. Without synthetic monitoring, the team would have found out about the outage from a spike in support tickets around 8 a.m., hours after the actual failure began.
Why It Matters for Reliability
Synthetic monitoring shifts detection left: instead of waiting for enough real users to be affected and complain, or for real user monitoring metrics to statistically cross a threshold, synthetic checks catch availability and correctness problems within minutes of them occurring, regardless of actual traffic volume. This directly improves mean time to detect (MTTD), which is often the largest component of overall incident duration. It’s also the most reliable way to continuously validate critical business transactions end-to-end, such as the full checkout flow, rather than just individual service health checks that might all report “healthy” while the overall user journey is broken.
How Teams Implement It
- Script checks around the most business-critical user journeys, not just a basic homepage ping, since a healthy homepage doesn’t guarantee checkout works.
- Run checks from multiple geographic regions to catch localized DNS, CDN, or regional infrastructure issues.
- Set alerting thresholds that require multiple consecutive failures or failures from multiple locations before paging, to avoid false positives from transient network blips.
- Combine synthetic monitoring with real user monitoring and distributed tracing, since a synthetic failure tells you something is broken, while tracing and RUM data help explain why and how many real users are affected.
- Include synthetic checks for third-party dependencies your system relies on, such as payment gateways or authentication providers, since their outages become your outages.
Limitations
Synthetic checks only test the specific paths they’re scripted to test; they can’t catch every possible failure mode a real, diverse user population might trigger, and scripts require ongoing maintenance as the application’s UI or API changes. Because synthetic traffic is predictable and repeated, it also sometimes misses issues that only manifest under real, varied load or with real, messy production data, which is why synthetic monitoring works best as one layer alongside RUM, APM, and distributed tracing rather than as a standalone monitoring strategy.
Frequently Asked Questions
What is Synthetic Monitoring?
Synthetic monitoring uses scripted, automated transactions run at regular intervals from external locations to proactively test whether a system's critical paths, like login or checkout, are available and performing correctly.
How does Synthetic Monitoring work?
Synthetic Monitoring works by combining the components described in the sections above. The main page walks through the architecture, the typical use cases, and the trade-offs to weigh before adopting it.
Why does Synthetic Monitoring matter?
Teams adopt Synthetic Monitoring to ship faster, run more reliably, and reduce the cognitive load on engineers. The benefits, limits, and adjacent tools are covered in the body above.
When should you use Synthetic Monitoring?
Use Synthetic Monitoring when the problems it solves match what your team is hitting today. The page above outlines the signals that mean you should adopt it now, and the cases where a simpler approach is fine.
