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If You're Not Monitoring Your APIs, You're Not Monitoring Your Applications

Denis Goodwin

The world we monitor has changed. This change all starts with shifts in software development, the Internet, and the expectations of end users — each evolving rapidly and because of each other. Software development has moved from one development team building end-to-end applications for a mostly homogenous set of users to many teams assembling software components into an application for a more diverse set of users.

The software development shift is driven in part by the growth of the Internet which demands scalable solutions that cannot be built and delivered by a single team and requires a distributed architecture. The reason we build all of this is to serve the needs of a variety of end users. Now, they have high expectations for software performance, led by the prevalence of consumer applications such as Facebook, Instagram, Twitter, YouTube, where everything is fast and mostly seamless.

The Impact of APIs on Application Performance

It is the confluence of these shifts that puts API performance front and center.

The way that software development becomes faster and more scalable is by using APIs to glue together components into applications. More scalable software development and delivery means more time to build the features that attract users. However, the assembled components delivered over a distributed architecture means that it can be tricky to provide the performance that end users expect since there are so many variables.

Since APIs Are Critical To Application Delivery, You Have To Monitor Them

The nature of how and what you monitor has to follow the same path as software development and delivery. Briefly, when software was developed end-to-end and was primarily distributed over a single network, you monitored the network by ping testing everything to make sure it was operating. As software moved outside the intranet, to the Internet, we began to monitor the entire application flow and find problems along the application delivery chain.

Today, there are a variety of monitoring methods that measure performance and availability of web applications from the back-end to the front-end, all to help operation teams manage software and developers to fix problems fast. The monitoring piece, which has been least implemented to date, is direct monitoring of APIs.

The picture that you currently have of your application performance goes blurry every time there is an API involved. If you don’t monitor the API, you can’t tell if a performance problem is in your application, the network, or the API itself. If you don’t monitor your third party APIs, you can’t tell if they are performing properly and within specifications, or if you should replace the API with one that can.

If you don’t monitor your APIs, you impact your Mean Time to Repair, which directly affects your bottom line.

Just as application creation and delivery has changed, application monitoring must change with it.

Denis Goodwin is Director of Product Management, APM, AlertSite UXM, SmartBear Software.

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If You're Not Monitoring Your APIs, You're Not Monitoring Your Applications

Denis Goodwin

The world we monitor has changed. This change all starts with shifts in software development, the Internet, and the expectations of end users — each evolving rapidly and because of each other. Software development has moved from one development team building end-to-end applications for a mostly homogenous set of users to many teams assembling software components into an application for a more diverse set of users.

The software development shift is driven in part by the growth of the Internet which demands scalable solutions that cannot be built and delivered by a single team and requires a distributed architecture. The reason we build all of this is to serve the needs of a variety of end users. Now, they have high expectations for software performance, led by the prevalence of consumer applications such as Facebook, Instagram, Twitter, YouTube, where everything is fast and mostly seamless.

The Impact of APIs on Application Performance

It is the confluence of these shifts that puts API performance front and center.

The way that software development becomes faster and more scalable is by using APIs to glue together components into applications. More scalable software development and delivery means more time to build the features that attract users. However, the assembled components delivered over a distributed architecture means that it can be tricky to provide the performance that end users expect since there are so many variables.

Since APIs Are Critical To Application Delivery, You Have To Monitor Them

The nature of how and what you monitor has to follow the same path as software development and delivery. Briefly, when software was developed end-to-end and was primarily distributed over a single network, you monitored the network by ping testing everything to make sure it was operating. As software moved outside the intranet, to the Internet, we began to monitor the entire application flow and find problems along the application delivery chain.

Today, there are a variety of monitoring methods that measure performance and availability of web applications from the back-end to the front-end, all to help operation teams manage software and developers to fix problems fast. The monitoring piece, which has been least implemented to date, is direct monitoring of APIs.

The picture that you currently have of your application performance goes blurry every time there is an API involved. If you don’t monitor the API, you can’t tell if a performance problem is in your application, the network, or the API itself. If you don’t monitor your third party APIs, you can’t tell if they are performing properly and within specifications, or if you should replace the API with one that can.

If you don’t monitor your APIs, you impact your Mean Time to Repair, which directly affects your bottom line.

Just as application creation and delivery has changed, application monitoring must change with it.

Denis Goodwin is Director of Product Management, APM, AlertSite UXM, SmartBear Software.

Hot Topics

The Latest

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 4 covers negative impacts of AI ...

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