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groundcover Integrates with OpenTelemetry

groundcover announced a deep integration with the popular open source project OpenTelemetry.

By combining groundcover's eBPF-driven context with OpenTelemetry's distributed tracing, DevOps and platform teams gain deeper insights into performance, user behavior, and errors across the stack.

"We firmly believe in the value of OpenTelemetry and the way it empowers organizations to generate, process, and flexibly collect and route traces in a standardized way," said Orr Benjamin, VP of Product at groundcover. "But it requires expertise and manual instrumentation that often leaves blind areas if not done 100% properly. eBPF solves that by providing automatic kernel-level instrumentation and deep information like the full payload of captured traces and relevant correlations to the infrastructure layer. Now engineers can have both the power of OpenTelemetry's distributed tracing and the deep instant coverage of eBPF in a single UX."

groundcover utilizes eBPF to provide deep observability into cloud-based architectures, using it to trace any type of event, from network and infrastructure, all the way to services and applications running in the user space. Using eBPF to collect observability data straight from the Linux kernel provides super-granular visibility into what's really happening inside modern applications.

By supporting OpenTelemetry, groundcover is embracing a more democratic approach to observability data that helps companies avoid vendor lock-in and to collect data in a standardized way. Unlike other observability vendors that require users to install and maintain a proprietary agent, groundcover works with OpenTelemetry out of the box, providing users with a single, seamless experience.

With this integration, an engineer can trace which services have spoken to one another with distributed tracing, and then leverage the power of eBPF to unlock new intelligence such as the user ID associated with the request, what device they used, and any associated error messages. In the past, this flow would require two sets of tools and dashboards, which have now been consolidated within groundcover's UI.

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.

groundcover Integrates with OpenTelemetry

groundcover announced a deep integration with the popular open source project OpenTelemetry.

By combining groundcover's eBPF-driven context with OpenTelemetry's distributed tracing, DevOps and platform teams gain deeper insights into performance, user behavior, and errors across the stack.

"We firmly believe in the value of OpenTelemetry and the way it empowers organizations to generate, process, and flexibly collect and route traces in a standardized way," said Orr Benjamin, VP of Product at groundcover. "But it requires expertise and manual instrumentation that often leaves blind areas if not done 100% properly. eBPF solves that by providing automatic kernel-level instrumentation and deep information like the full payload of captured traces and relevant correlations to the infrastructure layer. Now engineers can have both the power of OpenTelemetry's distributed tracing and the deep instant coverage of eBPF in a single UX."

groundcover utilizes eBPF to provide deep observability into cloud-based architectures, using it to trace any type of event, from network and infrastructure, all the way to services and applications running in the user space. Using eBPF to collect observability data straight from the Linux kernel provides super-granular visibility into what's really happening inside modern applications.

By supporting OpenTelemetry, groundcover is embracing a more democratic approach to observability data that helps companies avoid vendor lock-in and to collect data in a standardized way. Unlike other observability vendors that require users to install and maintain a proprietary agent, groundcover works with OpenTelemetry out of the box, providing users with a single, seamless experience.

With this integration, an engineer can trace which services have spoken to one another with distributed tracing, and then leverage the power of eBPF to unlock new intelligence such as the user ID associated with the request, what device they used, and any associated error messages. In the past, this flow would require two sets of tools and dashboards, which have now been consolidated within groundcover's UI.

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.