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You Have 40 Monitoring Tools, Make the Next One Count

Richard Whitehead
Moogsoft

In our growing digital economy, end users have no tolerance for downtime. Consequently, IT leaders invest heavily in availability: DevOps and SRE (site reliability engineering) teams to ensure digital apps and services are continuously available and digital tools built to influence uptime.

As recent research uncovered, IT leaders invest in a lot of single-domain monitoring tools. In fact, teams rely on an average of 16 monitoring tools — and up to 40 — according to the Moogsoft State of Availability Report.

Despite this heavy investment, teams are not achieving positive availability outcomes. Perhaps most telling, monitoring tools only catch performance issues or outages about half of the time. Customers flag the rest.

In other words, monitoring tool investments are not paying dividends. They are not helping teams quickly catch data anomalies and expediently fix incidents, and they certainly are not creating a positive customer experience. Yet, DevOps and SREs need monitoring solutions as manually monitoring ever-complex IT ecosystems with ever more data would be impossible.

So what's the secret to modern availability? How can teams better leverage their tools?

The Point Solution Problem: Partial Information

Part of the proliferation of monitoring tools in the IT stack is due to a proliferation of tools in the incident management space in general. Over the past few years, software vendors have introduced a slew of specific point solutions that solve specific problems.

On the positive side, point solutions specialize in monitoring certain aspects of an organization's IT ecosystem: the network, application, IT infrastructure or digital experience. But, problematically, point solutions do not integrate and cannot enable continuous insights across an IT stack. This siloed approach to monitoring:

Costs time and resources

Licensing copious amounts of monitoring tools is expensive. Perhaps even more expensive, human teams need to spend time managing and maintaining these monitoring solutions. And that is likely why research finds engineers spend more time monitoring over any other activity, innovation and value creation included.

Expands operational risk

Siloed approaches to anything — monitoring included — increase operational efficiencies and slow progress. When knowledge sits in one tool, the information tends to get orphaned and this lengthens communication lines and delays incident triage and resolution.

Increases downtime

Issues within the IT ecosystem are typically connected. But, because point solutions lack insight across the entire system, alerts tend to show up in multiple tools, creating a lot of unnecessary noise and further compounding and slowing incident remediation.

The Availability Answer: Use AIOps to Connect Monitoring Tools

To extract value out of monitoring tools and ensure more uptime, engineering teams need to connect their point solutions, creating a single line of sight across the entire incident lifecycle. Domain-agnostic artificial intelligence for IT operations (AIOps) can be this connective tissue. By converging data from all aspects of the incident lifecycle, AIOps connects otherwise siloed point solutions. This integrated approach to monitoring:

Provides a unified dashboard

Point solutions require engineers to hop from tool and tool, monitoring and maintaining various dashboards and charts. AIOps, on the other hand, integrates and aggregates data from across an organization's entire tool stack. As a result, engineering teams can look at one single dashboard that summarizes the health of all of their systems.

Streamlines the incident lifecycle

In addition to providing a summary of system health, AIOps solutions provide one single system of incident engagement. In this incident home base, engineering teams can track the incident lifecycle: detection, notification and resolution. Seeing the full picture of the incident lifecycle in one platform simplifies and speeds the response, and in the meantime, helps engineers understand — and then reduce — the amount of time each phase takes.

Optimizes overall systems

Because AIOps tools take a holistic approach to monitoring, they act as the connective tissue between an organization's monitoring data and help fill data gaps. These solutions make sense of data pulled from multiple point solutions, deduplicating and correlating alerts, enriching data and adding context across systems. This helps teams eliminate noise and identify root causes faster.

Instead of adding another point solution to a growing monitoring toolbox, IT leaders should make their next investment count. And AIOps could be the key. By adopting an AIOps tool, teams understand the whole picture of system health and can sidestep unnecessary noise and alerts to expediently respond to service-disrupting incidents. DevOps and SREs, facing less unplanned work, can invest in the future, paying down technical debt and further increasing system stability.

Richard Whitehead is Chief Evangelist at Moogsoft

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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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You Have 40 Monitoring Tools, Make the Next One Count

Richard Whitehead
Moogsoft

In our growing digital economy, end users have no tolerance for downtime. Consequently, IT leaders invest heavily in availability: DevOps and SRE (site reliability engineering) teams to ensure digital apps and services are continuously available and digital tools built to influence uptime.

As recent research uncovered, IT leaders invest in a lot of single-domain monitoring tools. In fact, teams rely on an average of 16 monitoring tools — and up to 40 — according to the Moogsoft State of Availability Report.

Despite this heavy investment, teams are not achieving positive availability outcomes. Perhaps most telling, monitoring tools only catch performance issues or outages about half of the time. Customers flag the rest.

In other words, monitoring tool investments are not paying dividends. They are not helping teams quickly catch data anomalies and expediently fix incidents, and they certainly are not creating a positive customer experience. Yet, DevOps and SREs need monitoring solutions as manually monitoring ever-complex IT ecosystems with ever more data would be impossible.

So what's the secret to modern availability? How can teams better leverage their tools?

The Point Solution Problem: Partial Information

Part of the proliferation of monitoring tools in the IT stack is due to a proliferation of tools in the incident management space in general. Over the past few years, software vendors have introduced a slew of specific point solutions that solve specific problems.

On the positive side, point solutions specialize in monitoring certain aspects of an organization's IT ecosystem: the network, application, IT infrastructure or digital experience. But, problematically, point solutions do not integrate and cannot enable continuous insights across an IT stack. This siloed approach to monitoring:

Costs time and resources

Licensing copious amounts of monitoring tools is expensive. Perhaps even more expensive, human teams need to spend time managing and maintaining these monitoring solutions. And that is likely why research finds engineers spend more time monitoring over any other activity, innovation and value creation included.

Expands operational risk

Siloed approaches to anything — monitoring included — increase operational efficiencies and slow progress. When knowledge sits in one tool, the information tends to get orphaned and this lengthens communication lines and delays incident triage and resolution.

Increases downtime

Issues within the IT ecosystem are typically connected. But, because point solutions lack insight across the entire system, alerts tend to show up in multiple tools, creating a lot of unnecessary noise and further compounding and slowing incident remediation.

The Availability Answer: Use AIOps to Connect Monitoring Tools

To extract value out of monitoring tools and ensure more uptime, engineering teams need to connect their point solutions, creating a single line of sight across the entire incident lifecycle. Domain-agnostic artificial intelligence for IT operations (AIOps) can be this connective tissue. By converging data from all aspects of the incident lifecycle, AIOps connects otherwise siloed point solutions. This integrated approach to monitoring:

Provides a unified dashboard

Point solutions require engineers to hop from tool and tool, monitoring and maintaining various dashboards and charts. AIOps, on the other hand, integrates and aggregates data from across an organization's entire tool stack. As a result, engineering teams can look at one single dashboard that summarizes the health of all of their systems.

Streamlines the incident lifecycle

In addition to providing a summary of system health, AIOps solutions provide one single system of incident engagement. In this incident home base, engineering teams can track the incident lifecycle: detection, notification and resolution. Seeing the full picture of the incident lifecycle in one platform simplifies and speeds the response, and in the meantime, helps engineers understand — and then reduce — the amount of time each phase takes.

Optimizes overall systems

Because AIOps tools take a holistic approach to monitoring, they act as the connective tissue between an organization's monitoring data and help fill data gaps. These solutions make sense of data pulled from multiple point solutions, deduplicating and correlating alerts, enriching data and adding context across systems. This helps teams eliminate noise and identify root causes faster.

Instead of adding another point solution to a growing monitoring toolbox, IT leaders should make their next investment count. And AIOps could be the key. By adopting an AIOps tool, teams understand the whole picture of system health and can sidestep unnecessary noise and alerts to expediently respond to service-disrupting incidents. DevOps and SREs, facing less unplanned work, can invest in the future, paying down technical debt and further increasing system stability.

Richard Whitehead is Chief Evangelist at Moogsoft

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.