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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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...