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Elastic Contributes Universal Profiling Agent to OpenTelemetry

Elastic announced the donation of its Universal Profiling agent has been accepted into OpenTelemetry (OTel)’s continuous profiling project. This marks a significant milestone in establishing profiling as the fourth telemetry signal in OpenTelemetry.

Elastic Universal Profiling is a whole-system, always-on, continuous profiling solution that eliminates the need for code instrumentation, recompilation, on-host debug symbols or service restarts. Leveraging eBPF, Elastic’s Universal Profiling agent profiles every line of code running on a machine, including application code, kernel, and third-party libraries. The solution measures code efficiency in three dimensions, CPU utilization, CO2, and cloud cost to help organizations manage efficient services by minimizing computational waste.

Unlike traditional profiling, which is often done only in a specific development phase or under controlled test conditions, continuous profiling runs in the background with minimal overhead. This provides real-time, actionable insights without replicating issues in separate environments. SREs, DevOps, and developers now have visibility into how code affects performance and cost, making code and infrastructure improvements easier.

“Our relationship with OTel continues to flourish, particularly in the last year where our donation of Elastic Common Schema, along with deep collaboration and mindshare with the OTel teams, have laid the foundation for successful, stable profiling,” said Abhishek Singh, general manager, observability, at Elastic. “The integration of Elastic Universal Profiling agent is another step forward in this journey and one that will help the global OTel community gain unprecedented visibility into fragmented, rapidly evolving application environments.”

Elastic’s Universal Profiling agent supports various runtimes and languages, such as C/C++, Rust, Zig, Go, Java, Python, Ruby, PHP, Node.js, V8, Perl, and .NET.

As part of the donation, Elastic will also provide a dedicated team of profiling domain experts to co-maintain and advance profiling capabilities within OTel.

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Elastic Contributes Universal Profiling Agent to OpenTelemetry

Elastic announced the donation of its Universal Profiling agent has been accepted into OpenTelemetry (OTel)’s continuous profiling project. This marks a significant milestone in establishing profiling as the fourth telemetry signal in OpenTelemetry.

Elastic Universal Profiling is a whole-system, always-on, continuous profiling solution that eliminates the need for code instrumentation, recompilation, on-host debug symbols or service restarts. Leveraging eBPF, Elastic’s Universal Profiling agent profiles every line of code running on a machine, including application code, kernel, and third-party libraries. The solution measures code efficiency in three dimensions, CPU utilization, CO2, and cloud cost to help organizations manage efficient services by minimizing computational waste.

Unlike traditional profiling, which is often done only in a specific development phase or under controlled test conditions, continuous profiling runs in the background with minimal overhead. This provides real-time, actionable insights without replicating issues in separate environments. SREs, DevOps, and developers now have visibility into how code affects performance and cost, making code and infrastructure improvements easier.

“Our relationship with OTel continues to flourish, particularly in the last year where our donation of Elastic Common Schema, along with deep collaboration and mindshare with the OTel teams, have laid the foundation for successful, stable profiling,” said Abhishek Singh, general manager, observability, at Elastic. “The integration of Elastic Universal Profiling agent is another step forward in this journey and one that will help the global OTel community gain unprecedented visibility into fragmented, rapidly evolving application environments.”

Elastic’s Universal Profiling agent supports various runtimes and languages, such as C/C++, Rust, Zig, Go, Java, Python, Ruby, PHP, Node.js, V8, Perl, and .NET.

As part of the donation, Elastic will also provide a dedicated team of profiling domain experts to co-maintain and advance profiling capabilities within OTel.

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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.