Synthetic testing uses automated scripts to simulate user interactions with a website, app, or API on a fixed schedule, checking that everything works correctly before real users encounter a problem. Rather than waiting for customers to report that a checkout flow is broken or a page is loading slowly, synthetic tests continuously run predefined scenarios and alert your team the moment something fails. It’s one of the most widely used approaches to proactive performance and availability monitoring.
How Synthetic Testing Works
At its core, synthetic testing sends artificial traffic through your application and checks the results. The simplest version is a script that pings a web address every few minutes and confirms it gets a successful response. More sophisticated tests open a real browser, click through a multi-step workflow (signing in, adding items to a cart, completing a purchase), and verify that each step loads correctly and within an acceptable timeframe.
There are three common approaches:
- Probing tools call your application’s endpoints on a fixed schedule using standard web protocols and check for a valid response. These are fast, lightweight, and good for basic uptime monitoring.
- Recorder-based flows capture a real person’s browser activity (clicks, form fills, navigation) and replay that activity on a schedule. This is the easiest way to create tests without writing code.
- Scripted browser tests use automation libraries like Playwright or Selenium to simulate user workflows through actual code. These are the most flexible option and can handle complex, multi-step transactions with conditional logic.
Selenium remains the dominant tool for this kind of end-to-end web testing, though Playwright has become the most prominent alternative and is growing quickly. Both tools launch a real browser engine, execute your script, and report timing data and failures at each step.
What Synthetic Tests Actually Measure
The traffic synthetic testing generates isn’t from real users. It’s artificially created traffic that collects data on page performance and application behavior under controlled conditions. Think of it as a laboratory environment: the same test runs from the same location, on the same schedule, with the same steps, so you get a clean baseline you can compare over time.
For web pages, synthetic tests typically measure how long each phase of loading takes: the initial server response, how quickly the page becomes visible, and when it’s fully interactive. For APIs, the checks go deeper. A well-built API synthetic test validates the status code (confirming the server returned a success response), checks that the response is properly formatted, validates the data structure to catch breaking changes, and measures latency against predefined thresholds. For example, a team might set a 500-millisecond ceiling for health checks and a 2-second ceiling for returning a list of orders, with any breach triggering an alert.
This kind of response schema validation is important because an API can return a “success” status code while delivering malformed or incomplete data. Synthetic tests catch that gap by parsing the actual response body and checking that every expected field is present and correctly typed.
Synthetic Testing vs. Real User Monitoring
Synthetic testing and real user monitoring (RUM) solve different problems. Synthetic tests run in a controlled environment on a schedule you define. RUM collects performance data from actual visitors as they use your site, capturing the full range of devices, network speeds, and geographic locations your real audience experiences.
The key tradeoff: synthetic testing catches problems before users do, but it only tests the specific scenarios you’ve scripted. RUM reveals problems you didn’t anticipate, like a performance issue that only appears on a particular mobile browser or in a region you didn’t think to test, but it can only report issues after real users have already experienced them. Most teams use both. Synthetic tests serve as an early warning system and regression safety net, while RUM fills in the gaps with real-world performance data.
Why Geographic Distribution Matters
Where your synthetic tests run from is just as important as what they test. A page that loads in 400 milliseconds from a data center in Virginia might take three seconds from Southeast Asia if your CDN isn’t configured correctly or a regional network provider is having issues. Running tests from multiple locations lets you segment performance by region and quickly identify whether a slowdown is global or isolated to a specific area.
Major synthetic monitoring platforms operate from hundreds or even thousands of checkpoint locations worldwide. This coverage lets teams validate application health across different network providers and geographic regions around the clock, catching problems that would be invisible from a single testing location.
Catching Problems During Deployment
One of the most practical uses of synthetic testing is integrating it directly into your deployment pipeline. The workflow looks like this: your code builds successfully, gets deployed to a staging environment, and then the pipeline automatically triggers a set of synthetic tests against that environment. If the tests pass, the deployment proceeds. If they fail, the pipeline rolls back the upgrade and marks the build as failed.
This approach catches regressions before they reach production. A broken login flow, a misconfigured API endpoint, or a performance degradation gets flagged automatically, without anyone needing to manually test the application after each release. The trend toward tighter integration with deployment pipelines is accelerating as teams ship code more frequently and can’t afford manual verification for every change.
Verifying Third-Party SLAs
If your product depends on external services (a payment processor, an identity provider, a third-party data API), you have a contractual service level agreement from that vendor but no visibility into their systems. Synthetic testing gives you independent verification. You set up tests that send real requests through the same paths your application uses in production, assert on the terms in your contract (response time, availability, error rate), and run those checks continuously.
This is especially valuable when a vendor dispute arises. Instead of relying on the provider’s own uptime dashboard, you have objective data collected from your environment showing exactly when their service fell below the agreed thresholds.
Where Synthetic Testing Is Heading
The synthetic monitoring market is projected to reach $3.36 billion by 2035, according to Precedence Research, with API and microservices monitoring growing as the fastest segment. The biggest shift underway is the use of AI and machine learning within synthetic monitoring platforms. These tools are starting to detect anomalies in test results, predict potential outages before they happen, and automate root-cause analysis when something does go wrong.
AI is also changing how tests get created. Rather than manually scripting every workflow, teams are using AI-assisted tools to generate test scripts from natural-language descriptions or existing API documentation. In the most advanced setups, AI-driven systems can trigger self-healing actions when a test fails, like rerouting traffic or restarting a service, without waiting for a human to respond. Healthcare and life sciences organizations are adopting synthetic monitoring at the fastest rate among industries, driven by the high cost of downtime in clinical and patient-facing systems.