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API Performance in 2016: New Insights for Organizations that Develop and Consume APIs

Priyanka Tiwari

When it comes to developing, deploying, and maintaining a truly powerful application, performance needs to be a top priority.

But that performance isn't only limited to the software your team builds and maintains. Moreover, the performance of an application depends on the performance of the APIs that power it.

SmartBear Software recently released the results of a global API survey, which includes responses from more than 2,300 software professionals in over 50 industries, across 104 countries around the globe.

The report included input from both API providers — organizations that develop and deploy APIs — and API consumers — organizations that use APIs to power their applications or internal systems.

When Asked: Why Do You Consume/Use APIs?

■ 50% said they use APIs to provide interoperation between internal systems, tools, and teams

■ 49% said they use APIs to extend functionality in a product or service

■ 42% said they use APIs to reduce development time

■ 38% said they used APIs to reduce development cost

It's clear to understand the impact that poor API performance could have on any of these use cases. Which is why it's not surprising that, when asked about how they would react upon encountering an API quality or performance issue, one-third of consumers said they would consider permanently switching API providers.

Whether you work in an organization that develops APIs, or have tools and systems that depend on APIs — performance should matter to you.

How Can You Ensure API Performance?

Just like you use tools to test and monitor your application, you also need to invest in the right tools for testing and monitoring your API. Whether you're launching an API of your own, or are concerned about the third party APIs that power your applications, you need to understand how your APIs are performing. You also need to understand the capacity of these APIs so that you can determine the amount of volume your applications can handle and adjust as necessary.

In most cases, ensuring API performance begins with load testing your API to ensure that it functions properly in real-world situations.

By utilizing specialized testing software, load testing allows testers to answer questions like:

"Is my system doing what I expect under these conditions?"

"How will my application respond when a failure occurs?"

"Is my application's performance good enough?"

But if you're performance strategy ends there, you could still be at risk of costly performance problems. This is where monitoring comes in.

API monitoring allows you to determine how your APIs are performing and compare those results to the performance expectations set for your application. Monitoring will enable you to collect insights that can then be incorporated back into the process. Once you've created your monitors and established your acceptable thresholds, you can set up alerts to be notified if performance degrades or the API goes offline.

Monitoring is Critical for Identifying and Resolving API Performance Issues

One of the key findings from the State of API 2016 Report is that a majority of API providers still face setbacks when it comes to resolving API performance issues.

Less than 10% of API issues are resolved within 24 hours. Nearly 1-in-4 API quality issues (23.9%) will remain unresolved for one week or more.

The biggest barrier to resolving API quality issues is determining the root cause (45.2%), followed by isolating the API as being the cause of the issue (29%).

A premium synthetic monitoring tool enables you to monitor your internal or 3rd party APIs proactively, from within your private network or from across the globe. A monitoring tool will help you find API and application issues, engage experts in a timely manner and fix issues before they impact your end users. If you are using external 3rd party APIs for your mission critical applications, a tool can help you monitor SLAs and hold your vendors accountable in case of unavailability or performance degradations.

Priyanka Tiwari is Product Marketing Manager, AlertSite, SmartBear Software.

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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 ...

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 ...

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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 ...

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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.

API Performance in 2016: New Insights for Organizations that Develop and Consume APIs

Priyanka Tiwari

When it comes to developing, deploying, and maintaining a truly powerful application, performance needs to be a top priority.

But that performance isn't only limited to the software your team builds and maintains. Moreover, the performance of an application depends on the performance of the APIs that power it.

SmartBear Software recently released the results of a global API survey, which includes responses from more than 2,300 software professionals in over 50 industries, across 104 countries around the globe.

The report included input from both API providers — organizations that develop and deploy APIs — and API consumers — organizations that use APIs to power their applications or internal systems.

When Asked: Why Do You Consume/Use APIs?

■ 50% said they use APIs to provide interoperation between internal systems, tools, and teams

■ 49% said they use APIs to extend functionality in a product or service

■ 42% said they use APIs to reduce development time

■ 38% said they used APIs to reduce development cost

It's clear to understand the impact that poor API performance could have on any of these use cases. Which is why it's not surprising that, when asked about how they would react upon encountering an API quality or performance issue, one-third of consumers said they would consider permanently switching API providers.

Whether you work in an organization that develops APIs, or have tools and systems that depend on APIs — performance should matter to you.

How Can You Ensure API Performance?

Just like you use tools to test and monitor your application, you also need to invest in the right tools for testing and monitoring your API. Whether you're launching an API of your own, or are concerned about the third party APIs that power your applications, you need to understand how your APIs are performing. You also need to understand the capacity of these APIs so that you can determine the amount of volume your applications can handle and adjust as necessary.

In most cases, ensuring API performance begins with load testing your API to ensure that it functions properly in real-world situations.

By utilizing specialized testing software, load testing allows testers to answer questions like:

"Is my system doing what I expect under these conditions?"

"How will my application respond when a failure occurs?"

"Is my application's performance good enough?"

But if you're performance strategy ends there, you could still be at risk of costly performance problems. This is where monitoring comes in.

API monitoring allows you to determine how your APIs are performing and compare those results to the performance expectations set for your application. Monitoring will enable you to collect insights that can then be incorporated back into the process. Once you've created your monitors and established your acceptable thresholds, you can set up alerts to be notified if performance degrades or the API goes offline.

Monitoring is Critical for Identifying and Resolving API Performance Issues

One of the key findings from the State of API 2016 Report is that a majority of API providers still face setbacks when it comes to resolving API performance issues.

Less than 10% of API issues are resolved within 24 hours. Nearly 1-in-4 API quality issues (23.9%) will remain unresolved for one week or more.

The biggest barrier to resolving API quality issues is determining the root cause (45.2%), followed by isolating the API as being the cause of the issue (29%).

A premium synthetic monitoring tool enables you to monitor your internal or 3rd party APIs proactively, from within your private network or from across the globe. A monitoring tool will help you find API and application issues, engage experts in a timely manner and fix issues before they impact your end users. If you are using external 3rd party APIs for your mission critical applications, a tool can help you monitor SLAs and hold your vendors accountable in case of unavailability or performance degradations.

Priyanka Tiwari is Product Marketing Manager, AlertSite, SmartBear Software.

Hot Topics

The Latest

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 ...

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 ...

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.