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Xangati ESP for Cloud Workspace and Cloud Infrastructure Introduced

Xangati announced a next-generation service assurance analytics and performance control platform, Xangati ESP for Cloud Workspace and Cloud Infrastructure, with self-healing support for new compute environments, new storage and networking data sources, and enhanced visibility into end-user metrics, spanning a range of software-dashboard modules for Virtual Infrastructure, Virtual Desktop Infrastructure (VDI) and Virtual Applications.

The Xangati ESP platform, a virtual appliance built on a common in-memory architecture that visualizes performance data with unprecedented speed and scale across multiple software-dashboard modules, crunches hundreds of thousands of interactions live, second-by-second, without the use of agents or probes, to enable predictive analytics based on dynamic threshold algorithms and machine-learned heuristics that determine acceptable service levels for:

- Performance: How fast is the infrastructure or platform running applications, and what impacts are there to the end-user experience?

- Efficiency: How well-utilized is the infrastructure or platform?

- Capacity: When does the infrastructure or platform need to be expanded to meet application needs to drive greater operations agility and productivity?

- Availability: Is the infrastructure or platform accessible/usable/active to mitigate risk to business disruptions or threat-related pattern anomalies?

- Cost: What are the right-sized IT investments to support the enterprise KPI’s and infrastructure ROI goals, and reasonable business outcomes?

Xangati launched four new ESP extensions to its best-in-class monitoring solution: NVIDIA physical GPU, Amazon Web Services (AWS), Microsoft Azure and Docker, in which Xangati’s virtual appliance software correlates real-time data across on-premise virtualization, containers and public clouds.

The Xangati ESP Extension for NVIDIA pGPU empowers cloud workspace system administrators with deep visibility into the performance of XenServer’s hypervisor pGPU utilization. Sysadmins can now optimize their pGPU resources by visualizing per-VM and percentage utilization metrics of pGPU, memory and frame buffers, and ensure optimal performance for VDI graphics acceleration.

The extensions for AWS, Azure and Docker are subscription licenses stackable on top of a required license for the core Xangati ESP for Cloud Infrastructure platform. The AWS and Azure Extensions analyze CPU, memory and storage utilization of an organization’s virtual machines and associated objects, and Virtual Private Cloud VMs hosted in an AWS or Azure account.

In a classic hybrid-cloud infrastructure, any VM can be a Docker-container host if the proper credentials to collect data are granted, such as for micro-services that execute one service or app (each Docker host consists of containers and images supporting a business service). The Xangati ESP Extension for Docker tracks and correlates metrics for containers and images so that they can be profiled and alerted upon within the overall context of end-to-end visibility and performance control services provided by Xangati.

From a networking perspective, Xangati is adding a new index for pattern anomalies, including end-user behavioral metrics that are correlated and analyzed across the spectrum of ESP modules and extensions:

- Measures unusual or suspicious activity associated with VMs, services and hosts

- Screens for anomalous interactions such as “affinity counts,” or the number of objects the anomaly index is interacting with; such anomalous interactions typically indicate potential security breaches (malware, DDoS, SpamBots, Data Leakage/Privacy)

- Comparison analysis benefits: The anomaly index is another useful source of threat intelligence for hybrid-cloud environments whereby data protection, data privacy and high availability are always top priorities.

Additional net new features and functionality to the Xangati ESP platform are:

- Xangati ESP Extension for EMC VNX: Deep integration into EMC VNX storage pools that reports the IOPS, throughput and latency metrics that EMC VNX is contributing to the hypervisors (file, block, unified). Additionally, data on NFS or CIFS shares, iSCSI or Fibre Channel LUNs and CPU utilization and overall network bit-rates are collected.

- Xangati ESP Storage Module: Designed for the storage infrastructure and virtual storage objects, Xangati collects metrics on the IOPS, throughput and latency of each datastore as well as the number of hypervisors and guest VMs using each. Xangati monitors the choke-points for storage systems, typically the network interfaces through which storage talks to the controllers that perform read/write transactions, the disks’ ability to deliver I/O, and/or the flash memory’s ability to cache and deliver I/O.

- Control enhancements of automated contention-storm analysis including for efficiency (alert driven remediation to avoid degrading conditions, and storm-driven remediation) and for prescriptive remediation actions (migration, modify memory or CPU, scale up or down, power off or on).

- Live visibility and on-demand insights of Citrix XenApp/XenDesktop and VMware Horizon availability and performance VDI metrics

- Device-independent, app-driven Visual Trouble Ticket capability

“Enterprises are focused on progressing their hybrid-cloud strategies; Xangati helps mitigate migration risk with its next-gen platform by extending its real-time performance analytics and IT efficiency solution beyond on-prem orthodoxies to include the two most popular public cloud resources, the predominant containerization standard, and several new data sources and analytics indices to create even greater gravity around one management console that assures service delivery quality and self-healing functions for virtual apps across conventional infrastructure silos,” said Atchison Frazer, CMO, Xangati.

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 ...

Xangati ESP for Cloud Workspace and Cloud Infrastructure Introduced

Xangati announced a next-generation service assurance analytics and performance control platform, Xangati ESP for Cloud Workspace and Cloud Infrastructure, with self-healing support for new compute environments, new storage and networking data sources, and enhanced visibility into end-user metrics, spanning a range of software-dashboard modules for Virtual Infrastructure, Virtual Desktop Infrastructure (VDI) and Virtual Applications.

The Xangati ESP platform, a virtual appliance built on a common in-memory architecture that visualizes performance data with unprecedented speed and scale across multiple software-dashboard modules, crunches hundreds of thousands of interactions live, second-by-second, without the use of agents or probes, to enable predictive analytics based on dynamic threshold algorithms and machine-learned heuristics that determine acceptable service levels for:

- Performance: How fast is the infrastructure or platform running applications, and what impacts are there to the end-user experience?

- Efficiency: How well-utilized is the infrastructure or platform?

- Capacity: When does the infrastructure or platform need to be expanded to meet application needs to drive greater operations agility and productivity?

- Availability: Is the infrastructure or platform accessible/usable/active to mitigate risk to business disruptions or threat-related pattern anomalies?

- Cost: What are the right-sized IT investments to support the enterprise KPI’s and infrastructure ROI goals, and reasonable business outcomes?

Xangati launched four new ESP extensions to its best-in-class monitoring solution: NVIDIA physical GPU, Amazon Web Services (AWS), Microsoft Azure and Docker, in which Xangati’s virtual appliance software correlates real-time data across on-premise virtualization, containers and public clouds.

The Xangati ESP Extension for NVIDIA pGPU empowers cloud workspace system administrators with deep visibility into the performance of XenServer’s hypervisor pGPU utilization. Sysadmins can now optimize their pGPU resources by visualizing per-VM and percentage utilization metrics of pGPU, memory and frame buffers, and ensure optimal performance for VDI graphics acceleration.

The extensions for AWS, Azure and Docker are subscription licenses stackable on top of a required license for the core Xangati ESP for Cloud Infrastructure platform. The AWS and Azure Extensions analyze CPU, memory and storage utilization of an organization’s virtual machines and associated objects, and Virtual Private Cloud VMs hosted in an AWS or Azure account.

In a classic hybrid-cloud infrastructure, any VM can be a Docker-container host if the proper credentials to collect data are granted, such as for micro-services that execute one service or app (each Docker host consists of containers and images supporting a business service). The Xangati ESP Extension for Docker tracks and correlates metrics for containers and images so that they can be profiled and alerted upon within the overall context of end-to-end visibility and performance control services provided by Xangati.

From a networking perspective, Xangati is adding a new index for pattern anomalies, including end-user behavioral metrics that are correlated and analyzed across the spectrum of ESP modules and extensions:

- Measures unusual or suspicious activity associated with VMs, services and hosts

- Screens for anomalous interactions such as “affinity counts,” or the number of objects the anomaly index is interacting with; such anomalous interactions typically indicate potential security breaches (malware, DDoS, SpamBots, Data Leakage/Privacy)

- Comparison analysis benefits: The anomaly index is another useful source of threat intelligence for hybrid-cloud environments whereby data protection, data privacy and high availability are always top priorities.

Additional net new features and functionality to the Xangati ESP platform are:

- Xangati ESP Extension for EMC VNX: Deep integration into EMC VNX storage pools that reports the IOPS, throughput and latency metrics that EMC VNX is contributing to the hypervisors (file, block, unified). Additionally, data on NFS or CIFS shares, iSCSI or Fibre Channel LUNs and CPU utilization and overall network bit-rates are collected.

- Xangati ESP Storage Module: Designed for the storage infrastructure and virtual storage objects, Xangati collects metrics on the IOPS, throughput and latency of each datastore as well as the number of hypervisors and guest VMs using each. Xangati monitors the choke-points for storage systems, typically the network interfaces through which storage talks to the controllers that perform read/write transactions, the disks’ ability to deliver I/O, and/or the flash memory’s ability to cache and deliver I/O.

- Control enhancements of automated contention-storm analysis including for efficiency (alert driven remediation to avoid degrading conditions, and storm-driven remediation) and for prescriptive remediation actions (migration, modify memory or CPU, scale up or down, power off or on).

- Live visibility and on-demand insights of Citrix XenApp/XenDesktop and VMware Horizon availability and performance VDI metrics

- Device-independent, app-driven Visual Trouble Ticket capability

“Enterprises are focused on progressing their hybrid-cloud strategies; Xangati helps mitigate migration risk with its next-gen platform by extending its real-time performance analytics and IT efficiency solution beyond on-prem orthodoxies to include the two most popular public cloud resources, the predominant containerization standard, and several new data sources and analytics indices to create even greater gravity around one management console that assures service delivery quality and self-healing functions for virtual apps across conventional infrastructure silos,” said Atchison Frazer, CMO, Xangati.

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 ...