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

Hannes Lenke
Checkly
Learn more about 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
Learn more about 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

Related Links

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Related Vendors

The Latest

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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