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Catchpoint Announces Major Synthetic Monitoring Enhancements

Catchpoint Systems announced a major release of its Synthetic Monitoring platform with new customizable dashboards and chart visualizations that enable organizations to easily connect performance metrics to business objectives.

The Cheetah release, available now to all Catchpoint customers, includes new features that allow organizations to quickly understand the performance impact of application changes before customers are affected, find relevant answers faster, and relay the results to the rest of the organization, showing how performance has a direct impact on business revenue. New features in Catchpoint’s Synthetic Monitoring release include:

- Enhanced Dashboards: Dashboards now allow customers to easily build customized views of real time data for different departments within their company, as well as incorporate Catchpoint dashboard data into other existing applications or intranets. These visualizations are flexible, highly interactive (drag and drop capabilities, TV ready, public sharing feature, etc.), and support multiple charting formats.

- Historical Comparisons: Unique to Catchpoint, this feature provides the ability to compare performance data over multiple time periods, pre- or post-event. This allows teams to clearly see what changed and the impact it had on performance and availability of websites and web applications.

- CDF Charting: This new (cumulative distribution function) charting feature allows users to instantly visualize what percentage of performance measurements are above or below any given response time. This allows targeting of specific problems without substantial investigations or calculations. Since this is cumulative, users are able to instantly visualize where in the distribution of performance measurements the outliers begin.

- Correlate Multiple Metrics: Customize up to 10 metrics into unique scatterplot diagrams that allow developers, DevOps, and operation teams to better visualize change and its impact, triage problems, as well as make decisions with greater confidence.

“The connection between performance and business objectives is a critical role of performance monitoring, and we now have the most visual, relevant and actionable solution set in the industry,” comments Mehdi Daoudi, CEO and co-founder of Catchpoint Systems. “With these advanced features, IT or development teams can share performance data throughout their organization and create a ‘dream dashboard’ of data relevant to their needs and the objectives of the company.”

Catchpoint’s Synthetic Monitoring Platform is backed by an industry leading array of over 380 international performance monitoring nodes, which provide accurate, clean-lab data free from the noise of variables outside of IT’s control. Web and infrastructure monitoring is available for websites and mobile, streaming, HTML code, DNS, APIs, server health and other IT resources.

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Catchpoint Announces Major Synthetic Monitoring Enhancements

Catchpoint Systems announced a major release of its Synthetic Monitoring platform with new customizable dashboards and chart visualizations that enable organizations to easily connect performance metrics to business objectives.

The Cheetah release, available now to all Catchpoint customers, includes new features that allow organizations to quickly understand the performance impact of application changes before customers are affected, find relevant answers faster, and relay the results to the rest of the organization, showing how performance has a direct impact on business revenue. New features in Catchpoint’s Synthetic Monitoring release include:

- Enhanced Dashboards: Dashboards now allow customers to easily build customized views of real time data for different departments within their company, as well as incorporate Catchpoint dashboard data into other existing applications or intranets. These visualizations are flexible, highly interactive (drag and drop capabilities, TV ready, public sharing feature, etc.), and support multiple charting formats.

- Historical Comparisons: Unique to Catchpoint, this feature provides the ability to compare performance data over multiple time periods, pre- or post-event. This allows teams to clearly see what changed and the impact it had on performance and availability of websites and web applications.

- CDF Charting: This new (cumulative distribution function) charting feature allows users to instantly visualize what percentage of performance measurements are above or below any given response time. This allows targeting of specific problems without substantial investigations or calculations. Since this is cumulative, users are able to instantly visualize where in the distribution of performance measurements the outliers begin.

- Correlate Multiple Metrics: Customize up to 10 metrics into unique scatterplot diagrams that allow developers, DevOps, and operation teams to better visualize change and its impact, triage problems, as well as make decisions with greater confidence.

“The connection between performance and business objectives is a critical role of performance monitoring, and we now have the most visual, relevant and actionable solution set in the industry,” comments Mehdi Daoudi, CEO and co-founder of Catchpoint Systems. “With these advanced features, IT or development teams can share performance data throughout their organization and create a ‘dream dashboard’ of data relevant to their needs and the objectives of the company.”

Catchpoint’s Synthetic Monitoring Platform is backed by an industry leading array of over 380 international performance monitoring nodes, which provide accurate, clean-lab data free from the noise of variables outside of IT’s control. Web and infrastructure monitoring is available for websites and mobile, streaming, HTML code, DNS, APIs, server health and other IT resources.

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.