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Zenoss Expands Monitoring and AIOps Capabilities for OpenStack Clouds

Zenoss released expanded monitoring capabilities for OpenStack, the most widely deployed open-source platform for building and managing private and public clouds.

Started by NASA and Rackspace in 2010, OpenStack is now one of the most active open-source projects in the world.

The Open Infrastructure Foundation (OIF) reports that the COVID-19 pandemic has brought new levels of demand for OpenStack-based public and private clouds, increasing the number of cores deployed by 66% year on year. According to foundation statistics, more than 100 new OpenStack clouds have been deployed in the last 18 months, and the total number of cores under OpenStack control now exceeds 25 million.

Globally, 71% of service providers are either in production or plan to be in production with OpenStack in the next 12 months, and Microsoft has recently joined the ranks of platinum-level members of the OIF.

Zenoss initially released monitoring and analytics capabilities for OpenStack in 2014 and has continuously expanded those capabilities to become the leading monitoring platform for OpenStack. Zenoss provides full-stack monitoring and AIOps for public and private OpenStack clouds and the most popular OpenStack components, including Nova, Neutron, Cinder, Swift and more. This features service impact modeling and root-cause analysis as well as the overall OpenStack state, including all tenants.

"The world of modern applications continues to transition toward ephemeral systems and automation," said Ani Gujrathi, CTO for Zenoss. “In this world, it’s even more critical to have full-stack visibility to ensure application health and performance, and Zenoss continues to lead the way in that endeavor.”

Zenoss Cloud is the leading AI-driven full-stack monitoring platform that streams and normalizes all machine data, uniquely enabling the emergence of context for preventing service disruptions in complex, modern IT environments, including those built on OpenStack and Kubernetes. Zenoss Cloud leverages the most powerful machine learning and real-time analytics to give companies the ability to scale and adapt to the changing needs of their businesses.

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Zenoss Expands Monitoring and AIOps Capabilities for OpenStack Clouds

Zenoss released expanded monitoring capabilities for OpenStack, the most widely deployed open-source platform for building and managing private and public clouds.

Started by NASA and Rackspace in 2010, OpenStack is now one of the most active open-source projects in the world.

The Open Infrastructure Foundation (OIF) reports that the COVID-19 pandemic has brought new levels of demand for OpenStack-based public and private clouds, increasing the number of cores deployed by 66% year on year. According to foundation statistics, more than 100 new OpenStack clouds have been deployed in the last 18 months, and the total number of cores under OpenStack control now exceeds 25 million.

Globally, 71% of service providers are either in production or plan to be in production with OpenStack in the next 12 months, and Microsoft has recently joined the ranks of platinum-level members of the OIF.

Zenoss initially released monitoring and analytics capabilities for OpenStack in 2014 and has continuously expanded those capabilities to become the leading monitoring platform for OpenStack. Zenoss provides full-stack monitoring and AIOps for public and private OpenStack clouds and the most popular OpenStack components, including Nova, Neutron, Cinder, Swift and more. This features service impact modeling and root-cause analysis as well as the overall OpenStack state, including all tenants.

"The world of modern applications continues to transition toward ephemeral systems and automation," said Ani Gujrathi, CTO for Zenoss. “In this world, it’s even more critical to have full-stack visibility to ensure application health and performance, and Zenoss continues to lead the way in that endeavor.”

Zenoss Cloud is the leading AI-driven full-stack monitoring platform that streams and normalizes all machine data, uniquely enabling the emergence of context for preventing service disruptions in complex, modern IT environments, including those built on OpenStack and Kubernetes. Zenoss Cloud leverages the most powerful machine learning and real-time analytics to give companies the ability to scale and adapt to the changing needs of their businesses.

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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.