Skip to main content

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

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

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

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...