Broadcom announced the availability of DX NetOps powered by Broadcom Silicon,an AI-driven, high scale operations monitoring and analytics solution.
Captured at the chip level for advanced network triage and remediation, DX NetOps delivers fine-grain per packet and flow level visibility to mitigate complex network congestion.
“Today’s businesses are now hyper-connected, reliant upon complex infrastructures, multi-cloud environments connecting billions of devices through a mesh of networks. This means more congestion and less visibility to triage the delivery of today’s digital experience,” said Serge Lucio, VP and GM, Enterprise Software Division, Broadcom. “In this complex environment, businesses demand a new approach to network monitoring to solve the new network congestion issue, one that is AI-driven, self-healing and powered by silicon.”
Broadcom provides next-generation high-scale operations monitoring that simplifies 5G, IoT, Cloud and SD-WAN deployments by applying AI and ML to the rich granular data captured at the chip level— enabling a unique automated AI-driven solution that remediates network congestion.
DX NetOps leverages AIOps from Broadcom and acts as a trusted proof point for the IT Operations analytics offered in BizOps from Broadcom. AIOps from Broadcom includes new intelligent recommendations and auto-remediation capabilities that help IT teams predict and prevent problems before they impact user experience.
“SD-WAN, 5G and edge compute technologies are key enablers for network agility but their dynamic nature introduces unique monitoring challenges, making it more difficult to detect, debug and remediate problems that degrade service delivery,” said Sudip Datta, head of AIOps and Monitoring, Enterprise Software Division, Broadcom. “DX NetOps powered by Broadcom silicon and BroadView™ Telemetry provides unparalleled granular, per-packet and flow-level insights that could potentially save our customers millions of dollars in lost revenue due to network bottlenecks.”
“Artificial Intelligence has the potential to fundamentally transform the networking landscape and deliver superior network optimization,” said Ram Velaga, SVP and GM, Core Switching Group, Broadcom. “AI capabilities critically depend on the quality and depth of the data. Powered by Broadcom silicon, BroadView Instrumentation delivers real-time network telemetry at packet level granularity in a scalable manner to differentiate the DX NetOps AI-driven next-gen network monitoring solution in the industry.”
Broadcom enhanced its analytic capabilities in the DX NetOps platform that now enables network operations teams to reliably deliver applications experiences over SD-WAN technologies built on Silver Peak, VMware (VeloCloud) and 128 Technology. Enhanced application-aware optimization technology allows enterprises to centralize real-time application monitoring across their multi-vendor SD-WAN deployments in a unified view that includes existing multi-vendor network devices, infrastructure layers and applications.
With the rise of modern architectures required to support digital business initiatives, it is imperative that enterprises have comprehensive visibility coupled with AI and machine learning capabilities to truly understand application behavior and optimize their SD-WAN investments.
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.