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

Denis Goodwin
Learn more about SmartBear

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
Learn more about SmartBear

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.

Related Links

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Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

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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 ...

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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.