
Gigamon announced new 100Gb product offerings and software updates to its portfolio that further extend the power of its market leading visibility solutions for high-performance enterprise networks, as well as 4G/5G LTE mobile operator infrastructure.
Enterprises and service providers are accelerating plans to upgrade to 100Gb Ethernet to support the high-bandwidth applications and systems that customers, business partners, subscribers and Internet of Things (IoT) implementers rely on and expect. For many of these upgrades, however, the traffic from high bandwidth links will far exceed the throughput of existing security and monitoring tools and applications.
Gigamon’s Visibility FabricTM with new 100Gb offerings resolves the speed mismatch while preserving existing investments in performance monitoring and network protection. Enterprises and service providers with current or planned 100Gb deployments can combine the benefits of the new 100Gb Gigamon portfolio with the software enhancements of the GigaVUE-OS and GigaVUE-FM Fabric Manager to help their new high performance networks maintain availability, compliance and security that is consistent with capabilities available prior to the upgrade.
Specifically, the new products introduced include:
- The GigaPORT-C06X24: A new interface module with six 100Gb ports and twenty-four 10Gb ports for the GigaVUE HD Series Visibility Fabric Node
- The GigaPORT-C02: A new interface module with two 100Gb ports for GigaVUE-HC2 Visibility Fabric Node
- The GigaVUE-TA100: A compact thirty-two 100Gb port system for efficient traffic aggregation in the Visibility Fabric
- The G-TAP M Series: Featuring high-density passive optical TAPs for non-intrusive traffic access to 100Gb short-reach and long-reach links
Concurrent with these new hardware products are more than 30 new capabilities introduced as part of GigaVUE-OS software 4.6, GigaVUE-FM Fabric Management software 3.3, and the GigaVUE-VM virtual software edition 3.3.
Key new capabilities introduced include:
- Metadata generation: Domain Name System (DNS) activity is appended to the NetFlow record for use in security analysis (i.e., detecting attackers and their lateral movement);
- 100Gb logical bypass: Enables inline security and availability of 100Gb networks;
- GPRS Tunneling Protocol (GTP) load balancing in clustered environments: Combines up to 32 nodes per cluster so that several Terabits of traffic in a mobile provider’s infrastructure can be efficiently correlated and distributed to security and performance applications;
- Enhanced topology visualization: Visualization of both the Visibility Fabric elements and the connected tools via GigaVUE-FM Fabric Manager;
- Real-time traffic visualization: Tracks traffic variations in real time on GigaVUE-FM Fabric Manager;
- Elastic search capability in GigaVUE-FM Fabric Manager: Enables quick search of the database for information and events of interest;
- Import/export topologies: From GigaVUE-FM Fabric Manager from/to Microsoft Excel; and
- OpenStack extensions: Include packet sampling of monitored traffic and simplified provider management of multi-tenant environments.
“Big data applications and meteoric growth in network traffic are driving 100G upgrades,” said Ananda Rajagopal, VP of Product Management, Gigamon. “Our Visibility Fabric ingests these massive traffic feeds and applies our patented traffic selection and intelligent optimization techniques, so that customers can significantly extend the effectiveness of existing security and performance monitoring efforts.”
The new 100Gb products are available immediately for pre-order and will ship in April 2016. The GigaVUE-TA100 will ship in May 2016. GigaVUE OS 4.6 and GigaVUE FM 3.3 are available for download now by those with active software support agreements.
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.