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ThousandEyes Announces Docker Container Support

ThousandEyes announced support for deployment of Enterprise Agents within Docker containers.

Docker containers are fast becoming the prevalent instrument for application development and deployment, and support for Docker container deployments enables organizations to automate deployment of ThousandEyes Enterprise Agents within their network. Now organizations can more rapidly extend network visibility from the branch office to the data center and facilitate expanded network visibility and insights in these highly dynamic environments.

"Docker and containers can offer a simpler, faster way to develop and deploy applications in today's fast-moving markets, but on their own they don't always have the accompanying tools and assurances in infrastructure management, networking and other areas that are needed and expected by enterprise end users," said Jay Lyman, Research Manager, Cloud Management and Containers at 451 Research. "Support for greater network visibility and assurance with Docker from organizations like ThousandEyes can help to close that gap and make use of containers and automation a reality for more organizations."

Docker containers are a simple and standardized way to build, deploy and run applications. With this support, ThousandEyes Enterprise Agents now more easily deploy within the organization's IT infrastructure delivering deep network performance insights to key services and applications traversing an organization's branches and data centers, as well as WAN and external providers. ThousandEyes' Docker support enables organizations to more rapidly deploy Enterprise Agents and build automated clusters across multiple locations, offering a valuable solution for DevOps, cloud-native companies, and other organizations moving to containerized environments.

"Cloud goes hand-in-hand with Docker containers. Organizations that utilize container technology typically employ DevOps processes and approaches with cloud technologies," said Nick Kephart, Head of Product Marketing at ThousandEyes. "As adoption of container technology matures and use cases expand, more organizations will deploy additional operational services using Docker. ThousandEyes provides visibility into the operations of the underlying network and its impact on the increasingly distributed applications that traverse it. Our support for Docker containers means that network operations teams can more easily and automatically deploy ThousandEyes Enterprise Agents anywhere and everywhere they need. Our DevOps and cloud-native customers will find a great deal of value in this new support."

ThousandEyes Enterprise Agents facilitate visibility and insight into network performance from within enterprise environments to critical services and applications. These agents help see into network links carrying VoIP and Unified Communications traffic, or into the MPLS or VPN links between enterprise sites. Additionally, customers who deploy agents in IaaS environments such as AWS, Azure or Google Compute Engine will find Docker to be an easy and efficient deployment method given their native support options.

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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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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.

ThousandEyes Announces Docker Container Support

ThousandEyes announced support for deployment of Enterprise Agents within Docker containers.

Docker containers are fast becoming the prevalent instrument for application development and deployment, and support for Docker container deployments enables organizations to automate deployment of ThousandEyes Enterprise Agents within their network. Now organizations can more rapidly extend network visibility from the branch office to the data center and facilitate expanded network visibility and insights in these highly dynamic environments.

"Docker and containers can offer a simpler, faster way to develop and deploy applications in today's fast-moving markets, but on their own they don't always have the accompanying tools and assurances in infrastructure management, networking and other areas that are needed and expected by enterprise end users," said Jay Lyman, Research Manager, Cloud Management and Containers at 451 Research. "Support for greater network visibility and assurance with Docker from organizations like ThousandEyes can help to close that gap and make use of containers and automation a reality for more organizations."

Docker containers are a simple and standardized way to build, deploy and run applications. With this support, ThousandEyes Enterprise Agents now more easily deploy within the organization's IT infrastructure delivering deep network performance insights to key services and applications traversing an organization's branches and data centers, as well as WAN and external providers. ThousandEyes' Docker support enables organizations to more rapidly deploy Enterprise Agents and build automated clusters across multiple locations, offering a valuable solution for DevOps, cloud-native companies, and other organizations moving to containerized environments.

"Cloud goes hand-in-hand with Docker containers. Organizations that utilize container technology typically employ DevOps processes and approaches with cloud technologies," said Nick Kephart, Head of Product Marketing at ThousandEyes. "As adoption of container technology matures and use cases expand, more organizations will deploy additional operational services using Docker. ThousandEyes provides visibility into the operations of the underlying network and its impact on the increasingly distributed applications that traverse it. Our support for Docker containers means that network operations teams can more easily and automatically deploy ThousandEyes Enterprise Agents anywhere and everywhere they need. Our DevOps and cloud-native customers will find a great deal of value in this new support."

ThousandEyes Enterprise Agents facilitate visibility and insight into network performance from within enterprise environments to critical services and applications. These agents help see into network links carrying VoIP and Unified Communications traffic, or into the MPLS or VPN links between enterprise sites. Additionally, customers who deploy agents in IaaS environments such as AWS, Azure or Google Compute Engine will find Docker to be an easy and efficient deployment method given their native support options.

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.