Choosing an APM Solution

New Ovum Decision Matrix Provides Guidance on APM
Michael Azoff

The market for Application Performance Management (APM) solutions continues to expand on the strength of innovation within the APM industry, resulting in new-generation tools, and also as a result of major shifts in IT usage around mobile and cloud, leading to demand for new APM capabilities.

To help IT decision-makers choose the right solution for their needs, the Ovum Decision Matrix on APM takes 10 of the leading APM solutions and evaluates and compares them side-by-side.

APM is Essential for Businesses, Providing Transparency into IT Applications and Infrastructure

APM is an essential activity for enterprises at multiple levels:

- During development, APM assists developers and QA staff with pre-release performance testing

- During live production, APM assists IT operations ensure mission-critical applications are running within the boundaries of SLAs (service-level agreements)

- APM supports the delivery of IT services to the business; advanced technology can preempt issues before end users are affected

- APM supports troubleshooting and defect-fixing when problems do occur

APM solutions monitor the IT environment, manage the gathering of metric data, and provide reports and dashboards for administrators, managers, and other stakeholders.

Cloud and Mobile Application Support is Essential for APM Solutions

APM remains a market with many different types of solutions, from hardware-based appliances to pure-software solutions. Typically, vendors approach the market with particular strengths and build out their coverage portfolio on top of these – for example, building solutions around complex event processing engines, or Big Data real-time analytics capabilities.

The market has seen a definite shift towards solutions supporting the latest mobile and cloud computing trends. As enterprises make better use of cloud services, and enterprise end users and consumers increasingly use smart mobile devices, the need to manage performance on these environments correspondingly grows. Another noticeable trend is the availability of APM-as-a-Service solutions.

Log Management Makes a Major Impact in APM

An emerging category within APM is log management, which has been growing at a strong pace with a number of new vendors joining the market; Splunk, in particular, has made quite a splash. These solutions mine the fields embedded in machine-generated data, including log files and headers in messages, and content that is generated by a host of applications from social network services such as Twitter, enterprise applications, and IT tools, including other APM solutions.

Log management tools process vast amounts of machine data in real time, exploiting Big Data technologies, so they represent a fusion of new technologies applied to existing categories of data. As the capabilities of log management tools are realized by users, developers write improved logs, creating a virtuous circle.

The incumbent APM vendors with Big Data capabilities are also addressing log management and are responding to this emerging solution category by better targeting their existing features.

To help enterprise IT users choose their APM tools, Ovum has recently published the Ovum Decision Matrix on APM 2014–15, which evaluates and compares 10 of the leading solutions in the market side-by-side.

Michael Azoff is a Principal Analyst at Ovum.

Related Links:

www.ovum.com

For Ovum Subscribers: Ovum Decision Matrix on APM 2014–15

Available from CA Technologies: Ovum Decision Matrix on APM 2014–15

Hot Topics

The Latest

Manufacturing Organizations Doubled AI Investment Yet Only 37% Fully Prepared to Operationalize AI

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Optimizing Decisions with Edge-First Cognitive Intelligence

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

The New Perimeter Is the User: Why Identity Is the Real Network Edge

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

When AI Infrastructure Stops Behaving Like Infrastructure

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Enterprise Resilience: Understanding the Shift from Static to Dynamic

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile Users Expect Perfection in 2026

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

AI Confidence Is High. Readiness Is Not. What the Data Tells Us - and Why It Matters

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI, Realistic Expectations and the Future of IT Operations in 2026

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

Trust, But Verify: Building Confidence in AI Through Human Oversight

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

Data Centers Plan to Reduce Reliance on Grid

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...

Choosing an APM Solution

New Ovum Decision Matrix Provides Guidance on APM
Michael Azoff

The market for Application Performance Management (APM) solutions continues to expand on the strength of innovation within the APM industry, resulting in new-generation tools, and also as a result of major shifts in IT usage around mobile and cloud, leading to demand for new APM capabilities.

To help IT decision-makers choose the right solution for their needs, the Ovum Decision Matrix on APM takes 10 of the leading APM solutions and evaluates and compares them side-by-side.

APM is Essential for Businesses, Providing Transparency into IT Applications and Infrastructure

APM is an essential activity for enterprises at multiple levels:

- During development, APM assists developers and QA staff with pre-release performance testing

- During live production, APM assists IT operations ensure mission-critical applications are running within the boundaries of SLAs (service-level agreements)

- APM supports the delivery of IT services to the business; advanced technology can preempt issues before end users are affected

- APM supports troubleshooting and defect-fixing when problems do occur

APM solutions monitor the IT environment, manage the gathering of metric data, and provide reports and dashboards for administrators, managers, and other stakeholders.

Cloud and Mobile Application Support is Essential for APM Solutions

APM remains a market with many different types of solutions, from hardware-based appliances to pure-software solutions. Typically, vendors approach the market with particular strengths and build out their coverage portfolio on top of these – for example, building solutions around complex event processing engines, or Big Data real-time analytics capabilities.

The market has seen a definite shift towards solutions supporting the latest mobile and cloud computing trends. As enterprises make better use of cloud services, and enterprise end users and consumers increasingly use smart mobile devices, the need to manage performance on these environments correspondingly grows. Another noticeable trend is the availability of APM-as-a-Service solutions.

Log Management Makes a Major Impact in APM

An emerging category within APM is log management, which has been growing at a strong pace with a number of new vendors joining the market; Splunk, in particular, has made quite a splash. These solutions mine the fields embedded in machine-generated data, including log files and headers in messages, and content that is generated by a host of applications from social network services such as Twitter, enterprise applications, and IT tools, including other APM solutions.

Log management tools process vast amounts of machine data in real time, exploiting Big Data technologies, so they represent a fusion of new technologies applied to existing categories of data. As the capabilities of log management tools are realized by users, developers write improved logs, creating a virtuous circle.

The incumbent APM vendors with Big Data capabilities are also addressing log management and are responding to this emerging solution category by better targeting their existing features.

To help enterprise IT users choose their APM tools, Ovum has recently published the Ovum Decision Matrix on APM 2014–15, which evaluates and compares 10 of the leading solutions in the market side-by-side.

Michael Azoff is a Principal Analyst at Ovum.

Related Links:

www.ovum.com

For Ovum Subscribers: Ovum Decision Matrix on APM 2014–15

Available from CA Technologies: Ovum Decision Matrix on APM 2014–15

Hot Topics

The Latest

Manufacturing Organizations Doubled AI Investment Yet Only 37% Fully Prepared to Operationalize AI

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Optimizing Decisions with Edge-First Cognitive Intelligence

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

The New Perimeter Is the User: Why Identity Is the Real Network Edge

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

When AI Infrastructure Stops Behaving Like Infrastructure

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Enterprise Resilience: Understanding the Shift from Static to Dynamic

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile Users Expect Perfection in 2026

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

AI Confidence Is High. Readiness Is Not. What the Data Tells Us - and Why It Matters

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI, Realistic Expectations and the Future of IT Operations in 2026

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

Trust, But Verify: Building Confidence in AI Through Human Oversight

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

Data Centers Plan to Reduce Reliance on Grid

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...