Skip to main content

New Requirements for Performance Management Vendors

Gabriel Lowy

Legacy performance management solutions were architected for smaller, less-complex and static computing environments that did not change much from year-to-year. When all an IT team had to worry about was measuring infrastructure availability and utilization these tools were sufficient. But time has passed them by.

Download the White Paper

As businesses become increasingly software-defined – and with the Internet of Things (IoT) on the near horizon – the pace of change has accelerated. Virtualization, agile development, cloud and mobility have given rise to modern globally distributed architectures and applications that result in an unprecedented level of scale, complexity and dynamism.

Modern applications have far greater connections points between the end user and the data center. They leverage shared services and compute resources that are managed centrally but may be controlled by either the enterprise or by external providers. However, many third-party cloud services are opaque, providing little visibility into the overall health of the compute infrastructure. These performance challenges extend to SaaS applications, which continue to proliferate within the enterprise.

Network and application performance issues have grown dramatically as these trends converge. This has caused more components of the application delivery chain to be obscured from IT and line of business owners. Poor performance issues increase the risk of user frustration.

Modern computing environments are driving a much greater need for end-to-end visibility. Availability and utilization metrics alone are no longer enough to understand infrastructure and application health and performance. Instead, IT teams must shift their focus from fault management and utilization to performance-based management in order to deliver better services consistently.

Widespread adoption of virtualization technologies and associated virtual machine migration, balancing between public, hybrid and private cloud environments and the traffic explosion of latency-sensitive applications such as market data, streaming video and voice-over-IP create new requirements for IT to achieve faster-time-to-value. Enterprises want to mitigate risk involved with new application rollouts, data center consolidation or physical-to-virtual migrations while ensuring consistent application performance that meets users’ expectations.

A New Generation of Performance Management Solutions

More enterprises have recognized the need for a new generation of performance management solutions that go beyond the scope of legacy monitoring tools to cut through this complexity. Modern tools should automatically detect and monitor all network assets, whether they are deployed on-premises, in the cloud or in hybrid environments. They should allow administrators to focus on higher-value tasks rather than on constantly watching the infrastructure and all connected systems.

Enterprises today have the following requirements for a next-generation performance management solution:

Easy-to-install; easy-to-use: For faster time to value, customers want solutions that work automatically after a simple installation without the need for professional services.

Fully-integrated views across multiple platforms: A unified view of metric, flow and time-stamped log data is valued for eliminating “swivel-chair monitoring” across disparate tools.

Rapidly scalable for all network devices, including non-SNMP: As the industry moves away from just supporting standard protocols like SNMP, a next-generation platform should intuitively scale to collect and store non-standard performance metrics from third-party sources.

Granularity of data: More enterprises are requiring high-frequency polling to allow second-by-second views of performance data.

Traps, alarms and alerts management: Real-time solutions can automatically baseline network performance for more meaningful and proactive monitoring. A unified platform also lets them consolidate conflicting consoles and alerts, further reducing the number of false positives while acceleration mean time to repair (MTTR).

Achieve business ROI and risk management objectives at lower TCO: Proactive analysis and troubleshooting help IT teams avoid service interruptions and outages, which can negatively impact business and expose the company to penalties. The increased automation in next-generation monitoring solutions reduces TCO while enabling IT to help business units improve outcomes and financial results

These products can help control costs, mitigate risk and enable faster time to value by enabling IT to positively impact the business. A modern-day solution should provide unified views of all data – including performance metrics, data flows and logs – to meet these new requirements. Enterprises can retire legacy server and application monitoring and reporting tools, saving unnecessary maintenance and operations costs.

A next-generation performance management platform enables customers to gain insights into understanding how their infrastructure is supporting core services. As a result, IT can better align with corporate objectives by improving business outcomes and financial performance.

Hot Topics

The Latest

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

New Requirements for Performance Management Vendors

Gabriel Lowy

Legacy performance management solutions were architected for smaller, less-complex and static computing environments that did not change much from year-to-year. When all an IT team had to worry about was measuring infrastructure availability and utilization these tools were sufficient. But time has passed them by.

Download the White Paper

As businesses become increasingly software-defined – and with the Internet of Things (IoT) on the near horizon – the pace of change has accelerated. Virtualization, agile development, cloud and mobility have given rise to modern globally distributed architectures and applications that result in an unprecedented level of scale, complexity and dynamism.

Modern applications have far greater connections points between the end user and the data center. They leverage shared services and compute resources that are managed centrally but may be controlled by either the enterprise or by external providers. However, many third-party cloud services are opaque, providing little visibility into the overall health of the compute infrastructure. These performance challenges extend to SaaS applications, which continue to proliferate within the enterprise.

Network and application performance issues have grown dramatically as these trends converge. This has caused more components of the application delivery chain to be obscured from IT and line of business owners. Poor performance issues increase the risk of user frustration.

Modern computing environments are driving a much greater need for end-to-end visibility. Availability and utilization metrics alone are no longer enough to understand infrastructure and application health and performance. Instead, IT teams must shift their focus from fault management and utilization to performance-based management in order to deliver better services consistently.

Widespread adoption of virtualization technologies and associated virtual machine migration, balancing between public, hybrid and private cloud environments and the traffic explosion of latency-sensitive applications such as market data, streaming video and voice-over-IP create new requirements for IT to achieve faster-time-to-value. Enterprises want to mitigate risk involved with new application rollouts, data center consolidation or physical-to-virtual migrations while ensuring consistent application performance that meets users’ expectations.

A New Generation of Performance Management Solutions

More enterprises have recognized the need for a new generation of performance management solutions that go beyond the scope of legacy monitoring tools to cut through this complexity. Modern tools should automatically detect and monitor all network assets, whether they are deployed on-premises, in the cloud or in hybrid environments. They should allow administrators to focus on higher-value tasks rather than on constantly watching the infrastructure and all connected systems.

Enterprises today have the following requirements for a next-generation performance management solution:

Easy-to-install; easy-to-use: For faster time to value, customers want solutions that work automatically after a simple installation without the need for professional services.

Fully-integrated views across multiple platforms: A unified view of metric, flow and time-stamped log data is valued for eliminating “swivel-chair monitoring” across disparate tools.

Rapidly scalable for all network devices, including non-SNMP: As the industry moves away from just supporting standard protocols like SNMP, a next-generation platform should intuitively scale to collect and store non-standard performance metrics from third-party sources.

Granularity of data: More enterprises are requiring high-frequency polling to allow second-by-second views of performance data.

Traps, alarms and alerts management: Real-time solutions can automatically baseline network performance for more meaningful and proactive monitoring. A unified platform also lets them consolidate conflicting consoles and alerts, further reducing the number of false positives while acceleration mean time to repair (MTTR).

Achieve business ROI and risk management objectives at lower TCO: Proactive analysis and troubleshooting help IT teams avoid service interruptions and outages, which can negatively impact business and expose the company to penalties. The increased automation in next-generation monitoring solutions reduces TCO while enabling IT to help business units improve outcomes and financial results

These products can help control costs, mitigate risk and enable faster time to value by enabling IT to positively impact the business. A modern-day solution should provide unified views of all data – including performance metrics, data flows and logs – to meet these new requirements. Enterprises can retire legacy server and application monitoring and reporting tools, saving unnecessary maintenance and operations costs.

A next-generation performance management platform enables customers to gain insights into understanding how their infrastructure is supporting core services. As a result, IT can better align with corporate objectives by improving business outcomes and financial performance.

Hot Topics

The Latest

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...