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2016 Application Performance Management Predictions - Part 5

Industry experts — from analysts and consultants to users and the top vendors — offer thoughtful, insightful, and often controversial predictions on how APM and related technologies will evolve and impact business in 2016. Part 5, the final installment, highlights the evolving IT environment, and offers a few predictions on the Application Performance Management market.

Start with 2016 Application Performance Management Predictions - Part 1

Start with 2016 Application Performance Management Predictions - Part 2

Start with 2016 Application Performance Management Predictions - Part 3

Start with 2016 Application Performance Management Predictions - Part 4

RELEASE QUALITY – THE NEW APPLICATION PERFORMANCE METRIC

In 2015, less than half of performance and development professionals used Release Quality as a baseline metric to judge application performance according to a report sponsored by HPE and blind survey executed by YouGov. This number is expected to rise in 2016 as organizations fully appreciate that there are no second chances with respect to making a strong first impression based on performance.
Todd DeCapua
Chief Technology Evangelist, Hewlett Packard Enterprise

2016 APM Prediction: Application Performance and Delivery Remain Top-of-Mind

API CHALLENGE DRIVES NEW APM, NPM AND ITOA TOOLS

Businesses will rely on APIs in their application stack more than ever, extending beyond internal business-related web services to infrastructure-related microservice APIs and external partner APIs. Development, QA and operations teams will face new challenges related to the integration of mission-critical services that are managed outside of their department, or even their organization. The concerns of these teams, when working with external service dependencies, are intertwined throughout the entire SDLC. Thus new tools will have to emerge that combine aspects of APM, NPM and ITOA relative to APIs, but more importantly, provide a common interface for easy cross-discipline collaboration between developers, testers and ops.
Neil Mansilla
VP of Developer Relations, Runscope

MICROSERVICES AND CONTAINERS REQUIRE REINVENTION OF APM

There will be increasing challenges for APM providers to be able to support the application (microservices), infrastructure (containers, SDx) and process changes (CI/CD) that are occurring in support of the needs of an increasingly digital business environment.
Cameron Haight
Research VP, IT Operations, Gartner

Perhaps the biggest disruption will be the continued adoption and evolution of microservices and containerization, which have really just started to become commodities. While microservices offer a lot of very compelling advantages, they also bring a new level of complexity to APM. The number of interacting components that make-up a system impacts all areas of troubleshooting, monitoring, logging, and debugging.
Sven Dummer
Senior Director of Product Marketing, Loggly

2016 will be the year where we see adoption of containers and microservices architectures truly skyrockets. It will be critical for APM solutions to provide additional and deeper metrics on containers performance. With modern containerized architectures gaining in terms of flexibility, speed and re-use, in a lot of cases the downside is a lot of additional complexity. APM solutions will need to focus on containers performance and how it maps to the overall application performance. There will be a greater need for higher visibility and additional levels of analytics for container-driven architectures.
Paola Moretto
Founder and CEO, Nouvola

APM has traditionally placed focus on user-facing services, such as web performance which took center stage in recent years. We are now in the rise of micro-service architectures, and in 2016, APM products will retool to focus on service-to-service performance analysis to better serve engineering teams.
Theo Schlossnagle
Founder and EVP, Products, Circonus

APM vendors will have to reinvent their products and business models to take advantage of and cope with Docker and containerization in general. Applications running on one JVM will get disaggregated into hundreds of micro-services, each of which talk to other micro-services on the same and different hosts. Transaction tracing between this "swarm" of micro-services will become essential. This is an enormous technical challenge that only leading edge APM vendors are equipped to meet. APM vendors will also have to revisit their pricing models as charging customers for each instance of each micro-service is simply not going to work.
Bernd Harzog
CEO, OpsDataStore

VENDOR OUTLOOK: CONSOLIDATION

As foreseen in previous years, the APM market continues to develop, both in terms of numbers of providers and inherent functionality, particularly with regard to end user visibility. However, a lack of fundamental differentiation between many of the providers means that consolidation is to be expected, either due to the collapse of (over geared) vendors, or via acquisition.
Larry Haig
Senior Consultant, Intechnica

APM Predictions 2016: Choosing an APM for Maximum Advantage

VENDOR OUTLOOK: THE BATTLE BETWEEN GROWTH PLAYERS AND STARTUPS

If we look at the market as falling into three categories: incumbent players, growth players, and startups, my prediction for 2016 is that the battle between growth players and startups will reach a feverish pitch. Growth players are driving toward going public, or perhaps having recently gone public, are driving overall customer growth. The younger startups in the space, in contrast, are seeking to flesh out differentiated products that will position them to take leadership positions away from growth players in 2017 and eventually from the incumbent players as well. But for 2016, the battle will be for mindshare between the growth players and startups.
Jason Bloomberg
President, Intellyx

Hot Topics

The Latest

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

2016 Application Performance Management Predictions - Part 5

Industry experts — from analysts and consultants to users and the top vendors — offer thoughtful, insightful, and often controversial predictions on how APM and related technologies will evolve and impact business in 2016. Part 5, the final installment, highlights the evolving IT environment, and offers a few predictions on the Application Performance Management market.

Start with 2016 Application Performance Management Predictions - Part 1

Start with 2016 Application Performance Management Predictions - Part 2

Start with 2016 Application Performance Management Predictions - Part 3

Start with 2016 Application Performance Management Predictions - Part 4

RELEASE QUALITY – THE NEW APPLICATION PERFORMANCE METRIC

In 2015, less than half of performance and development professionals used Release Quality as a baseline metric to judge application performance according to a report sponsored by HPE and blind survey executed by YouGov. This number is expected to rise in 2016 as organizations fully appreciate that there are no second chances with respect to making a strong first impression based on performance.
Todd DeCapua
Chief Technology Evangelist, Hewlett Packard Enterprise

2016 APM Prediction: Application Performance and Delivery Remain Top-of-Mind

API CHALLENGE DRIVES NEW APM, NPM AND ITOA TOOLS

Businesses will rely on APIs in their application stack more than ever, extending beyond internal business-related web services to infrastructure-related microservice APIs and external partner APIs. Development, QA and operations teams will face new challenges related to the integration of mission-critical services that are managed outside of their department, or even their organization. The concerns of these teams, when working with external service dependencies, are intertwined throughout the entire SDLC. Thus new tools will have to emerge that combine aspects of APM, NPM and ITOA relative to APIs, but more importantly, provide a common interface for easy cross-discipline collaboration between developers, testers and ops.
Neil Mansilla
VP of Developer Relations, Runscope

MICROSERVICES AND CONTAINERS REQUIRE REINVENTION OF APM

There will be increasing challenges for APM providers to be able to support the application (microservices), infrastructure (containers, SDx) and process changes (CI/CD) that are occurring in support of the needs of an increasingly digital business environment.
Cameron Haight
Research VP, IT Operations, Gartner

Perhaps the biggest disruption will be the continued adoption and evolution of microservices and containerization, which have really just started to become commodities. While microservices offer a lot of very compelling advantages, they also bring a new level of complexity to APM. The number of interacting components that make-up a system impacts all areas of troubleshooting, monitoring, logging, and debugging.
Sven Dummer
Senior Director of Product Marketing, Loggly

2016 will be the year where we see adoption of containers and microservices architectures truly skyrockets. It will be critical for APM solutions to provide additional and deeper metrics on containers performance. With modern containerized architectures gaining in terms of flexibility, speed and re-use, in a lot of cases the downside is a lot of additional complexity. APM solutions will need to focus on containers performance and how it maps to the overall application performance. There will be a greater need for higher visibility and additional levels of analytics for container-driven architectures.
Paola Moretto
Founder and CEO, Nouvola

APM has traditionally placed focus on user-facing services, such as web performance which took center stage in recent years. We are now in the rise of micro-service architectures, and in 2016, APM products will retool to focus on service-to-service performance analysis to better serve engineering teams.
Theo Schlossnagle
Founder and EVP, Products, Circonus

APM vendors will have to reinvent their products and business models to take advantage of and cope with Docker and containerization in general. Applications running on one JVM will get disaggregated into hundreds of micro-services, each of which talk to other micro-services on the same and different hosts. Transaction tracing between this "swarm" of micro-services will become essential. This is an enormous technical challenge that only leading edge APM vendors are equipped to meet. APM vendors will also have to revisit their pricing models as charging customers for each instance of each micro-service is simply not going to work.
Bernd Harzog
CEO, OpsDataStore

VENDOR OUTLOOK: CONSOLIDATION

As foreseen in previous years, the APM market continues to develop, both in terms of numbers of providers and inherent functionality, particularly with regard to end user visibility. However, a lack of fundamental differentiation between many of the providers means that consolidation is to be expected, either due to the collapse of (over geared) vendors, or via acquisition.
Larry Haig
Senior Consultant, Intechnica

APM Predictions 2016: Choosing an APM for Maximum Advantage

VENDOR OUTLOOK: THE BATTLE BETWEEN GROWTH PLAYERS AND STARTUPS

If we look at the market as falling into three categories: incumbent players, growth players, and startups, my prediction for 2016 is that the battle between growth players and startups will reach a feverish pitch. Growth players are driving toward going public, or perhaps having recently gone public, are driving overall customer growth. The younger startups in the space, in contrast, are seeking to flesh out differentiated products that will position them to take leadership positions away from growth players in 2017 and eventually from the incumbent players as well. But for 2016, the battle will be for mindshare between the growth players and startups.
Jason Bloomberg
President, Intellyx

Hot Topics

The Latest

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...