Skip to main content

ExtraHop Announces New Integrations with VMware

ExtraHop announced two new integrations with VMware's enterprise-ready, cloud management platform, VMware vRealize Suite.

The integrations with VMware vRealize Orchestrator and VMware vRealize Operations Manager provide mutual customers real-time visibility into the performance, availability and security of the Software-Defined Datacenter (SDDC).

"Software-defined datacenters offer unprecedented business agility as well as rich management data with industry standard API's for analytics across all layers of the OSI stack," said Rob Smoot, VP, Product Marketing, Cloud Management Business Unit, VMware. "Together, VMware and ExtraHop are combining infrastructure- and application-level data sources within VMware vRealize Operations Manager for analytics at the application protocol and port layers. This data is correlated with the virtualized compute, network, and storage information for enhanced visibility, troubleshooting, compliance, and capacity management."

With ExtraHop, VMware users can now automate the integration of wire data analytics into the VMware vRealize management architecture to gain real-time insight across the VMware software-defined data center, achieving the following benefits for enterprise IT:

- Dynamic On-Demand Visibility. The ExtraHop VMware vRealize Orchestrator Plugin makes it easy to automatically discover all VMware virtual machines and virtual switches as they are added to the environment, allowing users to classify and monitor these assets without any additional configuration.

- Intelligent Automation. ExtraHop alerts are used to trigger automated workflows within the VMware vRealize platform to improve agility and reduce management overhead.

- Centralized Management for Complete Insight. ExtraHop pulls rich wire data off of the network, and empowers customers to correlate insights with machine data, to deliver a complete view of virtualized environments and within a console VMware admins already use. This streamlined workflow gives visibility into the performance impact of SDDC orchestration in real time, and monitors application workloads as they migrate across the datacenter.

"SDDCs have the potential to unlock a new horizon of IT and business agility, but the realities of deployment and monitoring are not always straightforward and significantly limits the time to value," said John Leon, VP of Business Development at ExtraHop. "The extensive management capabilities of the vRealize suite, coupled with rich, real-time wire data from ExtraHop, rapidly provides customers with the global insights they need to take advantage of SDDC's promise and transform IT operations in the process."

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.

ExtraHop Announces New Integrations with VMware

ExtraHop announced two new integrations with VMware's enterprise-ready, cloud management platform, VMware vRealize Suite.

The integrations with VMware vRealize Orchestrator and VMware vRealize Operations Manager provide mutual customers real-time visibility into the performance, availability and security of the Software-Defined Datacenter (SDDC).

"Software-defined datacenters offer unprecedented business agility as well as rich management data with industry standard API's for analytics across all layers of the OSI stack," said Rob Smoot, VP, Product Marketing, Cloud Management Business Unit, VMware. "Together, VMware and ExtraHop are combining infrastructure- and application-level data sources within VMware vRealize Operations Manager for analytics at the application protocol and port layers. This data is correlated with the virtualized compute, network, and storage information for enhanced visibility, troubleshooting, compliance, and capacity management."

With ExtraHop, VMware users can now automate the integration of wire data analytics into the VMware vRealize management architecture to gain real-time insight across the VMware software-defined data center, achieving the following benefits for enterprise IT:

- Dynamic On-Demand Visibility. The ExtraHop VMware vRealize Orchestrator Plugin makes it easy to automatically discover all VMware virtual machines and virtual switches as they are added to the environment, allowing users to classify and monitor these assets without any additional configuration.

- Intelligent Automation. ExtraHop alerts are used to trigger automated workflows within the VMware vRealize platform to improve agility and reduce management overhead.

- Centralized Management for Complete Insight. ExtraHop pulls rich wire data off of the network, and empowers customers to correlate insights with machine data, to deliver a complete view of virtualized environments and within a console VMware admins already use. This streamlined workflow gives visibility into the performance impact of SDDC orchestration in real time, and monitors application workloads as they migrate across the datacenter.

"SDDCs have the potential to unlock a new horizon of IT and business agility, but the realities of deployment and monitoring are not always straightforward and significantly limits the time to value," said John Leon, VP of Business Development at ExtraHop. "The extensive management capabilities of the vRealize suite, coupled with rich, real-time wire data from ExtraHop, rapidly provides customers with the global insights they need to take advantage of SDDC's promise and transform IT operations in the process."

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.