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Why Synthetic Monitoring and End-to-End Testing Belong Together

Hannes Lenke
Checkly

Synthetic monitoring is crucial to deploy code with confidence as catching bugs with E2E tests on staging is becoming increasingly difficult. It isn't trivial to provide realistic staging systems, especially because today's apps are intertwined with many third-party APIs.

That's why nowadays, the low-hanging fruit is to set up checks that constantly monitor your production environment from an end-user perspective. This allows you to quickly find and fix issues on production before they become a problem for your customers. However, you need both testing in pre-production and monitoring on production.

Whether e-commerce shops or complex banking setups, systems are becoming increasingly intertwined and also distributed. They do not only rely on internal services but also on many external APIs such as payment APIs. It's nearly impossible to spin up production-like staging systems for these architectures. However, developers are, for many reasons, tasked to ship small pieces of new software numerous times a day. And this requires automation that ensures changes do not introduce bugs and break crucial flows while still delivering at speed and scale.

So on one side, we have complex systems that are nearly impossible to test fully in pre-production, and on the other, we have an increasing need for faster software delivery. These two things are like two trains on the same track heading for a collision. Thankfully, synthetic monitoring is here for the rescue!

But what is testing, and what is synthetic monitoring?

Let's look at synthetic monitoring and testing and what both could learn from each other. I'm sure ChatGPT can help us to define both terms:

Synthetic Monitoring

Synthetic monitoring tests and examines websites, applications, or services to ensure all components, including APIs, function as expected. It helps identify potential issues before they become a problem for the user or connected systems. It can be done from worldwide distributed remote locations. In simple terms, synthetic monitoring is having automated scripts checking your assets constantly to see if they are working correctly.

E2E testing

E2E testing helps to ensure the complete flow of an application or website works as expected before it gets deployed to production. It involves running tests to ensure all components work correctly from start to finish, as a real user would. In other words, it's like having an automated virtual tester check your web app to see if it works how it should.

Synthetics + Testing

In theory, synthetic monitoring and E2E testing are quite similar. While monitoring is meant to test your app on production constantly, E2E testing is intended to catch bugs before you deploy. The main difference in the past was that quality assurance (QA) teams performed testing while monitoring was the responsibility of operations (OPS), so the responsibility was split between two siloed teams. Not anymore!

Testing matured during the last decade from proprietary algorithms to open-source-based code hosted in your repository next to your application code. Today, cross-functional DevOps teams continuously run automated E2E tests in their CI/CD pipeline instead of isolated QA teams testing new versions of your app for three months before release.

Synthetic monitoring is also evolving similarly. It follows the transition E2E testing has already made: From proprietary scripts living in closed monitoring platforms to open-source-based scripts embedded in your repository. Monitoring is shifting left, as testing did, and is becoming integral to your developer's pipeline. The industry should encourage and enable developers to use the same scripts for pre-production tests and production monitoring. Doing so will blur the lines between E2E testing and synthetic monitoring.

So what does modern synthetic monitoring look like? Monitoring as code (MaC) is the next evolution of synthetic monitoring. To be successful in a MaC approach, we need to look at three essential pillars that make up the MaC concept: code, test, and deploy:

1. Code: Automated tests are defined as code and live in a repository, often close to your application code. When I write code, I mean code, not just configuration files saved in a repository. With that approach, MaC enables flexibility and programmability, allowing you to test your backend and UI by supporting complex API and browser checks.

2. Test: Synthetic monitoring was traditionally meant to run on production only. Now, checks as code enable us to run all or some of these checks locally and in a CI/CD flow to be tested on staging before a new version gets deployed. Monitoring is becoming testing, and testing is becoming monitoring, blurring the lines between the two.

3. Deploy: The main difference between testing and monitoring is scheduling. MaC enables us to schedule our tests, executing these constantly, 24/7, in distributed remote locations worldwide. In other words, your tests are deployable. In addition, deploying your tests via your CI/CD process allows monitors to be updated with application code changes.

Synthetic monitoring has been evolving quickly during the last months. We see many exciting approaches to enable developers to ensure that their apps are reliable and resilient. Monitoring as code is the only logical next step, as it has many advantages and enables you to reuse your tests.

Hannes Lenke is CEO and Co-Founder of Checkly

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Why Synthetic Monitoring and End-to-End Testing Belong Together

Hannes Lenke
Checkly

Synthetic monitoring is crucial to deploy code with confidence as catching bugs with E2E tests on staging is becoming increasingly difficult. It isn't trivial to provide realistic staging systems, especially because today's apps are intertwined with many third-party APIs.

That's why nowadays, the low-hanging fruit is to set up checks that constantly monitor your production environment from an end-user perspective. This allows you to quickly find and fix issues on production before they become a problem for your customers. However, you need both testing in pre-production and monitoring on production.

Whether e-commerce shops or complex banking setups, systems are becoming increasingly intertwined and also distributed. They do not only rely on internal services but also on many external APIs such as payment APIs. It's nearly impossible to spin up production-like staging systems for these architectures. However, developers are, for many reasons, tasked to ship small pieces of new software numerous times a day. And this requires automation that ensures changes do not introduce bugs and break crucial flows while still delivering at speed and scale.

So on one side, we have complex systems that are nearly impossible to test fully in pre-production, and on the other, we have an increasing need for faster software delivery. These two things are like two trains on the same track heading for a collision. Thankfully, synthetic monitoring is here for the rescue!

But what is testing, and what is synthetic monitoring?

Let's look at synthetic monitoring and testing and what both could learn from each other. I'm sure ChatGPT can help us to define both terms:

Synthetic Monitoring

Synthetic monitoring tests and examines websites, applications, or services to ensure all components, including APIs, function as expected. It helps identify potential issues before they become a problem for the user or connected systems. It can be done from worldwide distributed remote locations. In simple terms, synthetic monitoring is having automated scripts checking your assets constantly to see if they are working correctly.

E2E testing

E2E testing helps to ensure the complete flow of an application or website works as expected before it gets deployed to production. It involves running tests to ensure all components work correctly from start to finish, as a real user would. In other words, it's like having an automated virtual tester check your web app to see if it works how it should.

Synthetics + Testing

In theory, synthetic monitoring and E2E testing are quite similar. While monitoring is meant to test your app on production constantly, E2E testing is intended to catch bugs before you deploy. The main difference in the past was that quality assurance (QA) teams performed testing while monitoring was the responsibility of operations (OPS), so the responsibility was split between two siloed teams. Not anymore!

Testing matured during the last decade from proprietary algorithms to open-source-based code hosted in your repository next to your application code. Today, cross-functional DevOps teams continuously run automated E2E tests in their CI/CD pipeline instead of isolated QA teams testing new versions of your app for three months before release.

Synthetic monitoring is also evolving similarly. It follows the transition E2E testing has already made: From proprietary scripts living in closed monitoring platforms to open-source-based scripts embedded in your repository. Monitoring is shifting left, as testing did, and is becoming integral to your developer's pipeline. The industry should encourage and enable developers to use the same scripts for pre-production tests and production monitoring. Doing so will blur the lines between E2E testing and synthetic monitoring.

So what does modern synthetic monitoring look like? Monitoring as code (MaC) is the next evolution of synthetic monitoring. To be successful in a MaC approach, we need to look at three essential pillars that make up the MaC concept: code, test, and deploy:

1. Code: Automated tests are defined as code and live in a repository, often close to your application code. When I write code, I mean code, not just configuration files saved in a repository. With that approach, MaC enables flexibility and programmability, allowing you to test your backend and UI by supporting complex API and browser checks.

2. Test: Synthetic monitoring was traditionally meant to run on production only. Now, checks as code enable us to run all or some of these checks locally and in a CI/CD flow to be tested on staging before a new version gets deployed. Monitoring is becoming testing, and testing is becoming monitoring, blurring the lines between the two.

3. Deploy: The main difference between testing and monitoring is scheduling. MaC enables us to schedule our tests, executing these constantly, 24/7, in distributed remote locations worldwide. In other words, your tests are deployable. In addition, deploying your tests via your CI/CD process allows monitors to be updated with application code changes.

Synthetic monitoring has been evolving quickly during the last months. We see many exciting approaches to enable developers to ensure that their apps are reliable and resilient. Monitoring as code is the only logical next step, as it has many advantages and enables you to reuse your tests.

Hannes Lenke is CEO and Co-Founder of Checkly

Hot Topics

The Latest

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...