
Riverbed Technology announced the latest release of SteelCentral.
Riverbed SteelCentral now empowers customers to measure and troubleshoot all parts of the digital experience, from the user’s experience on the device to the back-end network, infrastructure, cloud and application.
The latest release of SteelCentral:
- Delivers enriched end user performance monitoring and provides integrated visibility into Digital Experience
- Reduces the risk during application migrations, both on and off the Cloud
- Enables businesses to manage outcomes across the application lifecycle
- Delivers integrated network and infrastructure troubleshooting and monitoring
“Our customers are making big ticket, highly strategic investments in digital business transformation initiatives to drive customer intimacy and employee/partner productivity. Delivering a flawless digital experience couldn’t be more critical to their success. But with the adoption of cloud and mobile technologies, they are finding that traditional tools are unable to holistically measure and manage a user’s digital experience,” said Mike Sargent, Senior Vice President and General Manager of SteelCentral at Riverbed. “SteelCentral now delivers the most complete, modular and integrated Digital Experience Management solution in the market, helping enterprises deliver a reliable and consistently high quality end user experience. With the breadth and depth of insight we now provide - down to the individual transaction level – we are taking visibility to a whole new level to help our customers achieve their strategic goals.”
This new release features the integration between SteelCentral Portal, SteelCentral Aternity, and SteelCentral AppInternals. This means that SteelCentral users can now incorporate the device-based view of end user experience providing IT and business executives with a single-pane-of-glass view of IT performance and its impact on end users.
In addition, the integrated workflow between SteelCentral Aternity and AppInternals provides an integrated monitoring system for the entire end user service and allows IT to rapidly troubleshoot business-critical applications across devices and applications. This results in a one-stop-shop for the variety of teams involved in Digital Experience Management, from end user services, to app developers and operations, to IT and business executives.
With this release, SteelCentral introduces application migration planning and prediction. This enables network planning and architecture teams to simulate and predict traffic behavior and impact on the network prior to application migrations – from data center to data center, from data center to cloud, and between cloud providers. As a result, companies are able to leverage data, not hunches, when planning cloud migrations for applications.
SteelCentral AppInternals now enables IT teams to consume performance insights and diagnostics across the application lifecycle. Leveraging new REST API’s, development and QA teams can add performance testing to their build tool chain and ensure that releases are optimized for production; operations teams can consume alerts on popular collaboration tools like Slack and HipChat; and support teams can automatically open tickets on incident management tools to log issues, their root causes and diagnoses. In addition, teams can use the API to extract metrics and enrich existing reports and tools.
Riverbed is also introducing a new integration between NetProfiler and NetIM that helps network managers understand the impact of network infrastructure on network performance. This integration is another example of how SteelCentral is enabling cross domain collaboration, breaking down the communication barriers created by the deployment of disjointed point monitoring solutions.
The Latest
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 ...
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 ...