Skip to main content

ThousandEyes Synthetics Introduced

ThousandEyes announced ThousandEyes Synthetics, an Internet-aware synthetic monitoring solution for proactive detection of modern application performance issues.

Addressing a significant application performance monitoring gap introduced by API-heavy and Internet-dependent application architectures, ThousandEyes Synthetics visually correlates application performance to underlying infrastructure and Internet delivery performance in a single, shareable dashboard for instant root cause identification and collaborative issue remediation. These new capabilities dramatically reduce business continuity risks, enable successful SaaS and cloud-hosted application rollouts, and give IT teams confidence in their ability to troubleshoot SaaS, cloud-based and browser-based application performance issues.

"Traditional synthetic monitoring solutions simply don't cut it in today's Internet-dependent, cloud-centric ecosystem. An app-centric view with no knowledge of the underlying dependencies leaves IT, Digital Ops and service delivery teams dead in the water while troubleshooting application performance issues," said ThousandEyes VP of Product, Joe Vaccaro. "ThousandEyes Synthetics enables both SaaS app owners and IT teams to deliver and deploy with confidence knowing they will be able to quickly identify exactly what's causing any issues in end-user digital experience regardless of where the issue lies, eliminating massive business continuity risks."

Legacy synthetics were designed for traditional application development approaches and for applications that are hosted in on-premises data centers where the Internet and third-party services aren't potential complicating factors for application performance. Modern applications, however, require an entirely new approach to synthetic monitoring due to the fact they are distributed, interact with multiple third-party services through APIs across multi-cloud environments, and are constantly changing thanks to continuous integration and continuous delivery (CI/CD) models.

ThousandEyes Synthetics combines a new, programmable javascript-based approach with deep active monitoring that correlates application insights gathered through synthetic tests with HTTP, network metrics, network paths, Internet routing, and outage visibility, in a single view. This allows for:

- Comprehensive Insights: Gain actionable insights into application performance with proactive monitoring of commonly trafficked user paths and multi-step business transactions.

- Cloud and Internet RCA: Expedite the identification of cloud- and Internet-related root causes of application performance issues like ISPs, CDNs, IaaS, SaaS, and the Internet.

- User-centric measurements: Understand how applications are performing from user-relevant locations via pre-deployed monitoring agent locations around the world and new mobile network monitoring agents.

"Our research shows that tool sprawl is a significant challenge for enterprise IT teams, and the need to reference multiple disparate tools and datasets makes addressing application performance issues incredibly difficult and time consuming, which has obvious impacts on end-user experience, revenue and brand reputation," said Shamus McGillicuddy, Research Director at EMA Research. "ThousandEyes' approach to consolidate and correlate different views of all the different potential sources of application performance issues will be very compelling for teams looking for that singular view, so time can be spent on issue remediation versus issue isolation."

The Latest

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...

ThousandEyes Synthetics Introduced

ThousandEyes announced ThousandEyes Synthetics, an Internet-aware synthetic monitoring solution for proactive detection of modern application performance issues.

Addressing a significant application performance monitoring gap introduced by API-heavy and Internet-dependent application architectures, ThousandEyes Synthetics visually correlates application performance to underlying infrastructure and Internet delivery performance in a single, shareable dashboard for instant root cause identification and collaborative issue remediation. These new capabilities dramatically reduce business continuity risks, enable successful SaaS and cloud-hosted application rollouts, and give IT teams confidence in their ability to troubleshoot SaaS, cloud-based and browser-based application performance issues.

"Traditional synthetic monitoring solutions simply don't cut it in today's Internet-dependent, cloud-centric ecosystem. An app-centric view with no knowledge of the underlying dependencies leaves IT, Digital Ops and service delivery teams dead in the water while troubleshooting application performance issues," said ThousandEyes VP of Product, Joe Vaccaro. "ThousandEyes Synthetics enables both SaaS app owners and IT teams to deliver and deploy with confidence knowing they will be able to quickly identify exactly what's causing any issues in end-user digital experience regardless of where the issue lies, eliminating massive business continuity risks."

Legacy synthetics were designed for traditional application development approaches and for applications that are hosted in on-premises data centers where the Internet and third-party services aren't potential complicating factors for application performance. Modern applications, however, require an entirely new approach to synthetic monitoring due to the fact they are distributed, interact with multiple third-party services through APIs across multi-cloud environments, and are constantly changing thanks to continuous integration and continuous delivery (CI/CD) models.

ThousandEyes Synthetics combines a new, programmable javascript-based approach with deep active monitoring that correlates application insights gathered through synthetic tests with HTTP, network metrics, network paths, Internet routing, and outage visibility, in a single view. This allows for:

- Comprehensive Insights: Gain actionable insights into application performance with proactive monitoring of commonly trafficked user paths and multi-step business transactions.

- Cloud and Internet RCA: Expedite the identification of cloud- and Internet-related root causes of application performance issues like ISPs, CDNs, IaaS, SaaS, and the Internet.

- User-centric measurements: Understand how applications are performing from user-relevant locations via pre-deployed monitoring agent locations around the world and new mobile network monitoring agents.

"Our research shows that tool sprawl is a significant challenge for enterprise IT teams, and the need to reference multiple disparate tools and datasets makes addressing application performance issues incredibly difficult and time consuming, which has obvious impacts on end-user experience, revenue and brand reputation," said Shamus McGillicuddy, Research Director at EMA Research. "ThousandEyes' approach to consolidate and correlate different views of all the different potential sources of application performance issues will be very compelling for teams looking for that singular view, so time can be spent on issue remediation versus issue isolation."

The Latest

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...