
Gartner has once again positioned Riverbed as a Leader in its 2019 Magic Quadrant for Network Performance Monitoring and Diagnostics, marking Riverbed's sixth consecutive year in a Leader position.
“While hybrid architectures are a reality for most modern enterprises, the majority of performance management tools available in the market are incapable of providing unified and user-centric visibility for hybrid cloud environments and the user’s digital experience. Riverbed SteelCentral is delivering end-to-end performance monitoring in high definition - and at a scale unmatched by any other NPMD vendor – six years named a Leader, we feel, is quite an accomplishment in the industry,” said Mike Sargent, SVP and GM of Riverbed’s SteelCentral business unit. “We believe our leadership position reflects the rich visibility we deliver into the network performance of hybrid and cloud environments, supported by time-saving troubleshooting workflows."
Riverbed offers digital experience management (DEM) and next generation infrastructure solutions to help organizations deliver superior performance. The Company’s DEM portfolio, SteelCentral, is focused on enabling the best digital experience for end users by providing organizations with comprehensive performance management and monitoring – from the network, to apps and digital services, to the end user’s experience on their devices.
With the latest release of SteelCentral, Riverbed introduced a new, comprehensive approach to monitor cloud performance that enables enterprises to monitor the digital experience of every application, in any cloud environment. Riverbed introduced the first cloud NPM solution that can both look broadly across cloud network traffic and deeply examine cloud network interactions. Utilizing both flow and packet-based approaches to network performance management (NPM), companies can build on existing on-premises expertise but now extend it to cloud environments.
Riverbed continued to address the challenges of hybrid IT, with cloud-resident monitoring using its advanced agent capabilities, as well as data source integrations with packet brokers and cloud service providers. Riverbed added alert management capabilities and updated the path analytics of its infrastructure monitoring solution. It also integrated monitoring with its market-leading SD-WAN solution to validate software-defined policy. Following the acquisition of security analytics vendor FlowTraq, Riverbed launched the NPM Advanced Security Module (ASM) that investigates and mitigates security threats that bypass traditional perimeter defenses.
The Latest
If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
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 ...