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ExtraHop Partners with Arista to Deliver Persistent Monitoring Architecture

ExtraHop announced a strategic technology partnership with Arista Networks and the launch of an integrated solution, the ExtraHop-Arista Persistent Monitoring Architecture.

This joint solution is built to accelerate the adoption of software-defined datacenter networking by delivering real-time, cross-tier, and cross-team persistent mobile visibility for dynamic environments.

The ExtraHop-Arista Persistent Monitoring Architecture capitalizes on Data ANalyZer (DANZ) capabilities in the Arista Extensible Operating System (EOS), delivering deep integration that enables IT teams to programmatically direct traffic to ExtraHop’s Context and Correlation Engine (CCE) for persistent mobile visibility derived from real-time IT operations analytics.

The joint solution dynamically adapts to changes in the software-defined datacenter, automatically discovering, classifying, and mapping dependencies of all applications and infrastructure and their performance.

This functionality is accomplished without the use of agents, delivering persistent L2–L7 visibility across all network, infrastructure, application, and end-user transactions before, during, and after changes are made to the environment.

This deep, cross-tier visibility dramatically reduces the risks associated with the continuous provisioning, de-provisioning, and migration of VMs in the datacenter.

The ExtraHop-Arista Persistent Monitoring Architecture includes the following key features:

- Persistent and continuous auto-discovery of virtual and physical servers and their associated MAC addresses using ExtraHop’s API, ensuring that Arista’s persistent SPAN capabilities maintain their identity regardless of port or VLAN migration

- Real-time reassembly of full sessions, flows, and transactions, in addition to classification of devices based on ongoing heuristic analysis of MAC addresses, IP addresses, wire protocols, transaction types, and other wire data

- Seamless, persistent visibility during vMotion events, including the spinning up, spinning down, and migration of virtual machines across the datacenter

“The collaboration between ExtraHop and Arista on this joint solution continues both companies’ commitment to excellence,” said Ed Chapman, Vice President of Business Development and Alliances, Arista Networks. “Paired with ExtraHop’s technology, the DANZ features in Arista EOS are able to provide a seamless and persistent SDN solution that eliminates lack of application visibility within high-stakes virtualized environments.”

“In many ways, virtualization and software-defined datacenters can simplify the management of IT operations. However, many organizations find that when applications are divorced from dedicated infrastructure and the network, traditional approaches to monitoring performance, availability, and security no longer work,” said Jesse Rothstein, CEO, ExtraHop Networks. “Through our partnership with Arista, we’re helping IT teams tackle these challenges. Together, ExtraHop and Arista offer these organizations a solution purpose-built to deliver the persistent visibility they need to reap the full benefits of virtualized, software-defined environments, and we’re doing it at one-tenth the cost of legacy-based offerings.”

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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 ...

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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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

ExtraHop Partners with Arista to Deliver Persistent Monitoring Architecture

ExtraHop announced a strategic technology partnership with Arista Networks and the launch of an integrated solution, the ExtraHop-Arista Persistent Monitoring Architecture.

This joint solution is built to accelerate the adoption of software-defined datacenter networking by delivering real-time, cross-tier, and cross-team persistent mobile visibility for dynamic environments.

The ExtraHop-Arista Persistent Monitoring Architecture capitalizes on Data ANalyZer (DANZ) capabilities in the Arista Extensible Operating System (EOS), delivering deep integration that enables IT teams to programmatically direct traffic to ExtraHop’s Context and Correlation Engine (CCE) for persistent mobile visibility derived from real-time IT operations analytics.

The joint solution dynamically adapts to changes in the software-defined datacenter, automatically discovering, classifying, and mapping dependencies of all applications and infrastructure and their performance.

This functionality is accomplished without the use of agents, delivering persistent L2–L7 visibility across all network, infrastructure, application, and end-user transactions before, during, and after changes are made to the environment.

This deep, cross-tier visibility dramatically reduces the risks associated with the continuous provisioning, de-provisioning, and migration of VMs in the datacenter.

The ExtraHop-Arista Persistent Monitoring Architecture includes the following key features:

- Persistent and continuous auto-discovery of virtual and physical servers and their associated MAC addresses using ExtraHop’s API, ensuring that Arista’s persistent SPAN capabilities maintain their identity regardless of port or VLAN migration

- Real-time reassembly of full sessions, flows, and transactions, in addition to classification of devices based on ongoing heuristic analysis of MAC addresses, IP addresses, wire protocols, transaction types, and other wire data

- Seamless, persistent visibility during vMotion events, including the spinning up, spinning down, and migration of virtual machines across the datacenter

“The collaboration between ExtraHop and Arista on this joint solution continues both companies’ commitment to excellence,” said Ed Chapman, Vice President of Business Development and Alliances, Arista Networks. “Paired with ExtraHop’s technology, the DANZ features in Arista EOS are able to provide a seamless and persistent SDN solution that eliminates lack of application visibility within high-stakes virtualized environments.”

“In many ways, virtualization and software-defined datacenters can simplify the management of IT operations. However, many organizations find that when applications are divorced from dedicated infrastructure and the network, traditional approaches to monitoring performance, availability, and security no longer work,” said Jesse Rothstein, CEO, ExtraHop Networks. “Through our partnership with Arista, we’re helping IT teams tackle these challenges. Together, ExtraHop and Arista offer these organizations a solution purpose-built to deliver the persistent visibility they need to reap the full benefits of virtualized, software-defined environments, and we’re doing it at one-tenth the cost of legacy-based offerings.”

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...