ExtraHop announced the fifth generation of its platform.
ExtraHop 5.0 delivers turnkey stream analytics for wire data, enabling organizations to automatically discover devices, systems, and their relationships; observe and measure their behavior; and explore this data to unlock cross-domain insights that extend across the business.
- Operations teams are empowered to accelerate Internet of Things (IoT) initiatives with agentless, incremental monitoring of dynamic environments and connected devices.
- Network and IT security teams can rapidly correlate all north/south, and east/west traffic, whether in the datacenter or the cloud. This visibility enables teams to drill down to easily identify anomalous and disruptive behavior from any device or user, speeding insight into how that behavior is impacting the performance, availability, and security of the infrastructure, improving mean time to resolution and ensuring persistent visibility.
- Line of Business stakeholders can access granular insight into individual user experiences, enabling them to better track, monitor, and improve that experience to drive revenue and improve brand perception.
"The incredible rate of technology adoption in the enterprise is ushering in a new era, transforming IT from a support center to a force multiplier for business," said Jesse Rothstein, CEO, ExtraHop. "The fifth generation of the ExtraHop platform is designed to empower this transformation, enabling for the first time truly data-driven operations. Our platform allows organizations to discover, explore, and take command of their network, client, application, and business data in a single platform, delivering unprecedented insights that drive collaboration, understanding, and value."
The fifth generation of the ExtraHop platform offers robust new multi-dimensional search and analytics features:
- The ExtraHop Explore appliance empowers IT and business stakeholders to query, investigate, and correlate standard or custom-defined historical metrics. When coupled with the real-time, full-stream analytics of the ExtraHop Discover Appliance, users have a multi-dimensional view into the most voluminous and accurate source of IT and business data.
- Open Data Stream for Kafka extends the power of the company's first-to-market open architecture to support the correlation of multiple data sets and streamlines the distribution of those data sets to multiple destinations.
- Dynamic Discovery allows for the automatic discovery of any device in the environment (including IoT connected devices), understands device dependencies, and tracks activity without instrumentation. L2 tunneling enables monitoring and analysis of virtual-machine-to-virtual-machine traffic, including virtual L2 segments such as SDN and private cloud. Expanded protocol support for DHCP, Telnet, Kerberos, and MSMQ, provides deeper insight across the environment.
- Universal Observation delivers continuous, comprehensive observation of the IT environment from the highest level to granular, second-by-second detail to help IT identify anomalous and disruptive behavior from any device or user in real time.
- A REST API leverages the ExtraHop platform's comprehensive understanding of all user, application, network, and business activity for smarter orchestration and automation. For the first time in the industry, users can programmatically use, control, and administrate any physical or virtual appliance through any programming language.
- New user interface and user experience in version 5.0 are designed to make the richest data set in IT available to all users by simplifying and accelerating the time to exploration and discovery. Key new features include a visual query language, making search and data pivots simple and accessible to all users; dynamic tables for rapidly building comparisons of any transaction attribute; and selective dashboard sharing including the ability to handpick eligible users. New global navigation dynamically guides users through metrics, pivots quickly between options, and easily navigates back through history. A "recent pages" option provides a bookmark-like history for easy look-back navigation.
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.