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Circonus Partners with StormForge on Kubernetes Performance

Circonus and StormForge announced a partnership to offer combined solutions. The companies' complementary Kubernetes offerings empower customers to improve performance, minimize downtime, and reduce costs. Circonus’ Kubernetes monitoring solution enables users to immediately surface actionable insights into the health and performance of their clusters. Customers can install the solution in minutes, and turnkey alerting and dashboards are available out-of-the-box with zero set-up or configuration required. Purpose-built for Kubernetes, the Circonus Kubernetes monitoring solution provides health-check insights such as crash loops, job and volume failures, and pod pending delays. Alerts instantly send notifications when clusters become unhealthy, and guided issue remediation pinpoints root causes to speed problem resolution. StormForge, formerly known as Carbon Relay, combines cloud-native performance testing with machine-learning powered optimization to help DevOps teams proactively ensure performance, reliability, and cost-efficiency. The StormForge Platform uses machine learning to automatically test, analyze, and optimize Kubernetes applications based on your cost and performance goals. The StormForge Platform can be integrated into an organization’s CI/CD workflow to build automated, continuous performance testing and optimization into the release process. The result is the highest and most reliable performance possible at the lowest costs. StormForge’s proactive optimization coupled with Circonus’s health and performance monitoring enable a full lifecycle of Kubernetes performance and cost optimization. Organizations can use StormForge’s predictive intelligence to proactively minimize risk and issues, and then feed analytics from Circonus into StormForge’s machine learning for ongoing performance predictions and optimization. Key benefits of the combined solutions include: - Improve performance: Proactive performance testing and ongoing health-check monitoring together minimize the number of potential issues and maximize performance for end users. - Reduce cloud costs: Predictive intelligence and resource utilization analysis can significantly lower Kubernetes costs. - Improve operational efficiency: Speed DevOps workflows while also reducing alerts, downtime, and mean time to resolution (MTTR). - Simplify the complexities of Kubernetes: Automated optimization and turnkey monitoring removes many of the time-consuming complexities of Kubernetes. Matt Provo, CEO, StormForge, said: “We immediately realized that Circonus and StormForge took a similar approach to our solutions, which is to help companies optimize their Kubernetes performance in a way that’s easy and efficient. We remove the complexities of Kubernetes and together provide a complete, ongoing circle of Kubernetes performance and cost optimization, making it easier for companies to fully realize the benefits Kubernetes offers.” Bob Moul, CEO, Circonus, added: “Combining the powerful monitoring and rich telemetry data of Circonus with the magic of AI and machine learning from StormForge delivers total operational clarity and predictable cost and performance to Kubernetes deployments.”

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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.

Circonus Partners with StormForge on Kubernetes Performance

Circonus and StormForge announced a partnership to offer combined solutions. The companies' complementary Kubernetes offerings empower customers to improve performance, minimize downtime, and reduce costs. Circonus’ Kubernetes monitoring solution enables users to immediately surface actionable insights into the health and performance of their clusters. Customers can install the solution in minutes, and turnkey alerting and dashboards are available out-of-the-box with zero set-up or configuration required. Purpose-built for Kubernetes, the Circonus Kubernetes monitoring solution provides health-check insights such as crash loops, job and volume failures, and pod pending delays. Alerts instantly send notifications when clusters become unhealthy, and guided issue remediation pinpoints root causes to speed problem resolution. StormForge, formerly known as Carbon Relay, combines cloud-native performance testing with machine-learning powered optimization to help DevOps teams proactively ensure performance, reliability, and cost-efficiency. The StormForge Platform uses machine learning to automatically test, analyze, and optimize Kubernetes applications based on your cost and performance goals. The StormForge Platform can be integrated into an organization’s CI/CD workflow to build automated, continuous performance testing and optimization into the release process. The result is the highest and most reliable performance possible at the lowest costs. StormForge’s proactive optimization coupled with Circonus’s health and performance monitoring enable a full lifecycle of Kubernetes performance and cost optimization. Organizations can use StormForge’s predictive intelligence to proactively minimize risk and issues, and then feed analytics from Circonus into StormForge’s machine learning for ongoing performance predictions and optimization. Key benefits of the combined solutions include: - Improve performance: Proactive performance testing and ongoing health-check monitoring together minimize the number of potential issues and maximize performance for end users. - Reduce cloud costs: Predictive intelligence and resource utilization analysis can significantly lower Kubernetes costs. - Improve operational efficiency: Speed DevOps workflows while also reducing alerts, downtime, and mean time to resolution (MTTR). - Simplify the complexities of Kubernetes: Automated optimization and turnkey monitoring removes many of the time-consuming complexities of Kubernetes. Matt Provo, CEO, StormForge, said: “We immediately realized that Circonus and StormForge took a similar approach to our solutions, which is to help companies optimize their Kubernetes performance in a way that’s easy and efficient. We remove the complexities of Kubernetes and together provide a complete, ongoing circle of Kubernetes performance and cost optimization, making it easier for companies to fully realize the benefits Kubernetes offers.” Bob Moul, CEO, Circonus, added: “Combining the powerful monitoring and rich telemetry data of Circonus with the magic of AI and machine learning from StormForge delivers total operational clarity and predictable cost and performance to Kubernetes deployments.”

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.