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GroundWork Releases BoxSpy Container Monitoring for Dynamic Docker and Linux Container

GroundWork releases GroundWork BoxSpy, designed specifically to monitor Docker and other Linux container environments.

GroundWork BoxSpy is the most resource-efficient, comprehensive monitoring system available for today’s container technologies.

“Docker and containers represent a newer, often better way for developers, lines of business and organizations to develop and package applications, but for enterprise IT teams there are still significant gaps in terms of security and other concerns compared to traditional VMs,” says Jay Lyman, Research Manager for 451 Research. “One of the areas that is lacking for Docker and containers is monitoring, where BoxSpy can help provide the capabilities required for enterprise deployment.”

Benefits of monitoring Docker containers with GroundWork BoxSpy:

- Works ‘out-of-the-box’ with GroundWork, but also has a REST API that makes it compatible with other monitoring technologies

- Lives in a container and is capable of monitoring other containers running on the same system

- Designed for both DevOps and production environments

- Adds full-featured enterprise monitoring to Docker environments with the ability to correlate Docker performance with the rest of the IT environment

- Designed for dynamic environments — automatically monitors new Docker containers as you spin them up

“Dynamic environments, like those based on Linux containers, tend to break IT monitoring or at best render them cumbersome and complicated. You can’t be very dynamic if your management tools can’t keep up with the speed of change,” said David Dennis, VP of Marketing and Products for GroundWork. “Also, running Linux containers in production requires the ability to see container performance data next to performance data from the rest of the infrastructure – the compute, network and storage components. If you can’t do that, you can’t optimize your application scale out. We’re happy to have worked with Docker personnel to make BoxSpy solve both problems.”

BoxSpy is based on Google’s cAdvisor container monitoring technology and improves it by:

- Dramatically reducing the resource demand and performance impact

- API clean-up and bug-fixing

- Removing unnecessary overhead that isn’t needed when talking to an external monitoring system

- Normalizing metrics to make them more human readable and standardized

- Adding threshold setting

- Adding process monitoring

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GroundWork Releases BoxSpy Container Monitoring for Dynamic Docker and Linux Container

GroundWork releases GroundWork BoxSpy, designed specifically to monitor Docker and other Linux container environments.

GroundWork BoxSpy is the most resource-efficient, comprehensive monitoring system available for today’s container technologies.

“Docker and containers represent a newer, often better way for developers, lines of business and organizations to develop and package applications, but for enterprise IT teams there are still significant gaps in terms of security and other concerns compared to traditional VMs,” says Jay Lyman, Research Manager for 451 Research. “One of the areas that is lacking for Docker and containers is monitoring, where BoxSpy can help provide the capabilities required for enterprise deployment.”

Benefits of monitoring Docker containers with GroundWork BoxSpy:

- Works ‘out-of-the-box’ with GroundWork, but also has a REST API that makes it compatible with other monitoring technologies

- Lives in a container and is capable of monitoring other containers running on the same system

- Designed for both DevOps and production environments

- Adds full-featured enterprise monitoring to Docker environments with the ability to correlate Docker performance with the rest of the IT environment

- Designed for dynamic environments — automatically monitors new Docker containers as you spin them up

“Dynamic environments, like those based on Linux containers, tend to break IT monitoring or at best render them cumbersome and complicated. You can’t be very dynamic if your management tools can’t keep up with the speed of change,” said David Dennis, VP of Marketing and Products for GroundWork. “Also, running Linux containers in production requires the ability to see container performance data next to performance data from the rest of the infrastructure – the compute, network and storage components. If you can’t do that, you can’t optimize your application scale out. We’re happy to have worked with Docker personnel to make BoxSpy solve both problems.”

BoxSpy is based on Google’s cAdvisor container monitoring technology and improves it by:

- Dramatically reducing the resource demand and performance impact

- API clean-up and bug-fixing

- Removing unnecessary overhead that isn’t needed when talking to an external monitoring system

- Normalizing metrics to make them more human readable and standardized

- Adding threshold setting

- Adding process monitoring

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.