F5 announced continued support for Cloud Native Computing Foundation’s OpenTelemetry, an open source framework providing a standardized collection of tools to instrument, generate, capture, and export telemetry data (metrics, logs, and traces) to help IT teams better analyze their solutions’ performance and behavior.
In a development partnership with ServiceNow, F5 has contributed approximately 40,000 lines of code to double the compressibility of the OpenTelemetry Protocol. This innovation reduces the costs of exporting data from data centers, clouds, and edge locations to a telemetry platform by half, depending on the specific data workload. To put the size of this contribution in perspective, the typical software developer produces between 10,000 and 25,000 lines of code each year and the average open source project comprises around 35,000 lines of code.
Optimizing the collection, ingestion, and analysis of telemetry data such as metrics, logs, and traces enables organizations to more efficiently and performantly deliver intelligent, automated responses amidst variable IT conditions. As an example, telemetry can help security teams detect and prevent fraudulent and nefarious activities by comparing login and behavioral characteristics of users and applications against typical usage patterns. Customers who export such telemetry data from public clouds to a telemetry aggregation system using this new refinement to the OpenTelemetry Protocol can expect their egress costs related to telemetry traffic to be reduced by 50%.
“Modern networking solutions, applications, and users generate unfathomably large volumes of data in the course of everyday operations, making compression rates essential to bringing the benefits of comprehensive telemetry to more organizations in a cost-effective manner,” said Laurent Quérel, project lead and distinguished engineer at F5. “While today’s enterprises are much better suited to capture and analyze data versus their historical counterparts, challenges have persisted around effective transport, centralization, and standardization, which is why F5 has invested in the OpenTelemetry project. By adopting OpenTelemetry across the F5 portfolio—and continuing to participate in leading open source efforts with organizations like ServiceNow—we see limitless use cases to make applications more adaptive to changes in their runtime environment via performance assessment, added efficiencies, and enhanced forensics while keeping customer costs to a minimum.”
Beginning with attempts to further optimize the OpenTelemetry Protocol in the context of Apache Arrow to significantly increase processing speed and reduce bandwidth costs, F5 partnered with ServiceNow Cloud Observability to introduce the OpenTelemetry Arrow Project and reference implementation guidance. Apache Arrow is an open source technology enhancing big data, analytics, and machine learning used to provide standardized representations of structured and semi-structured data in a more actionable, digestible format and promote advanced telemetry capabilities.
For many organizations, transporting and processing such data efficiently accounts for a significant portion of the cost associated with telemetry pipelines. Standardizing telemetry is especially critical because sufficient observability and AI-driven analysis require a uniform intake of data points from the entire IT stack. Protocol enhancements and standards developed and refined within an open source community provide surrounding industries with a powerful ecosystem of projects and solution infrastructure to build out tailored capabilities specific to their organizations and users. This approach also gives F5 customers heightened visibility into the immediate factors impacting their applications, better positioning IT teams to make strategic investments over time.
“When Lightstep co-founded the OpenTelemetry project, our goal was to work within the open source community to help enterprises maximize visibility without increasing spend,” said Daniel “Spoons” Spoonhower, chief architect at ServiceNow Cloud Observability, formerly known as Lightstep. “Through this work with F5, we’re continuing to execute on what enterprises need—the ability to ship more data at a lower cost.”
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.