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Zenoss Releases Unified Monitoring for OpenStack Swift Cloud Storage Platform

complete monitoring of OpenStack Object Storage infrastructure

Zenoss, a provider of management software for physical, virtual, and cloud-based IT infrastructures, announced the availability of unified monitoring for the OpenStack Swift cloud storage platform.

Available as a free and open source "ZenPack," the new capability extends Zenoss Core with deep instrumentation of the components that comprise OpenStack Swift and links this with health and configuration information of the underlying server, network and storage infrastructure. The result is end-to-end visibility into the health and capacity of the delivery environment, allowing cloud service providers to rapidly solve problems and proactively manage service levels.

OpenStack Object Storage (code-named Swift) is open source software for creating redundant, scalable object storage using clusters of standardized servers to store petabytes of accessible data. As OpenStack matures, more OpenStack-based clouds are being deployed into production. Running Swift without visibility into the underlying infrastructure is reckless because there are several well-known conditions that are irrecoverable and will lead to data loss.

Before the OpenStack Swift ZenPack, there was nothing for operators to ensure service levels because no specialized, best-practice solution for OpenStack monitoring existed. Zenoss’ OpenStack Swift ZenPack allows Swift-based cloud operators to ensure proper functioning of their storage clouds, and determine whether the cluster is able to perform within given key SLAs. As a result, operators can confirm that cluster and data integrity aren’t compromised by failing disks or object quarantines, cluster requests are fulfilled within given time periods, and cluster capacity meets both short and long-term needs.

“As a founding member of the OpenStack project we’re extremely excited to contribute to the project,” said Bill Karpovich, CEO of Zenoss. “With the OpenStack Swift ZenPack and its holistic approach we are especially trying to help providers manage their cloud infrastructure and meet their SLAs.”

Click here to download the OpenStack Swift ZenPack.

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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 ...

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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 ...

Zenoss Releases Unified Monitoring for OpenStack Swift Cloud Storage Platform

complete monitoring of OpenStack Object Storage infrastructure

Zenoss, a provider of management software for physical, virtual, and cloud-based IT infrastructures, announced the availability of unified monitoring for the OpenStack Swift cloud storage platform.

Available as a free and open source "ZenPack," the new capability extends Zenoss Core with deep instrumentation of the components that comprise OpenStack Swift and links this with health and configuration information of the underlying server, network and storage infrastructure. The result is end-to-end visibility into the health and capacity of the delivery environment, allowing cloud service providers to rapidly solve problems and proactively manage service levels.

OpenStack Object Storage (code-named Swift) is open source software for creating redundant, scalable object storage using clusters of standardized servers to store petabytes of accessible data. As OpenStack matures, more OpenStack-based clouds are being deployed into production. Running Swift without visibility into the underlying infrastructure is reckless because there are several well-known conditions that are irrecoverable and will lead to data loss.

Before the OpenStack Swift ZenPack, there was nothing for operators to ensure service levels because no specialized, best-practice solution for OpenStack monitoring existed. Zenoss’ OpenStack Swift ZenPack allows Swift-based cloud operators to ensure proper functioning of their storage clouds, and determine whether the cluster is able to perform within given key SLAs. As a result, operators can confirm that cluster and data integrity aren’t compromised by failing disks or object quarantines, cluster requests are fulfilled within given time periods, and cluster capacity meets both short and long-term needs.

“As a founding member of the OpenStack project we’re extremely excited to contribute to the project,” said Bill Karpovich, CEO of Zenoss. “With the OpenStack Swift ZenPack and its holistic approach we are especially trying to help providers manage their cloud infrastructure and meet their SLAs.”

Click here to download the OpenStack Swift ZenPack.

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 ...