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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

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

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

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...