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Circonus Announces Availability of Spring 2020 Release

Circonus announced its Spring 2020 release. The highlight of the release is a Kubernetes monitoring solution that provides health-based alerting and horizontal pod auto-scaling. Additional enhancements include cloud monitoring, GCP Marketplace availability, performance improvements, and a more comprehensive Terraform integration. With these latest enhancements, Circonus becomes the only solution that can collect and analyze IT infrastructure, application, and container data at extreme scale across cloud, on-prem, and hybrid deployments — all in one platform. The Circonus Kubernetes monitoring solution lets enterprises monitor one or more Kubernetes clusters. It provides turnkey dashboards and alerting for increased visibility into cluster performance and health, and drives dynamic Kubernetes horizontal pod auto-scaling strategies that can be custom-tuned to the unique needs of an organization. Features include: - Easy-to-install agent - Immediate, real-time operational and health dashboards - Turnkey alerting for Kubernetes clusters to ensure Kubernetes remains healthy - Health-check insights such as crash loops, disk and memory pressure, job and volume failures, errors, pod pending delays, and deployment glitches - Native StatsD collection support - Horizontal pod auto-scaling What makes auto-scaling with Circonus unique is the ability to drive auto-scaling based on rich historical data analysis that customers have stored within Circonus — driving efficient, novel auto-scaling in a way that no other solution currently can. As a result, organizations can create dynamic auto-scaling strategies that will ensure optimal performance, reduce costs, and save time. Circonus’s cloud monitoring solution includes a cloud agent that provides a lightweight binary through which to easily collect metrics from Amazon Web Services (AWS), Azure, and Google Cloud Platform (GCP), as well as in-application dashboards. Customers can instantly move all of their metrics from multiple clouds into a single platform, enabling them to run queries, set alerts, and compare data from a single place. The Spring release also includes: - Performance improvements: Customers can now run analytic queries upon thousands of application metrics in near real time for unprecedented visibility into application performance - Comprehensive Terraform integration: Extension, documentation, and other improvements have been made to Circonus’s native Terraform provider - High availability metric ingestion: The Circonus cluster broker is designed to share the load of metric ingestion across multiple brokers, creating a fail-safe if one goes down - UI refresh: The UI has experienced a refresh with the addition of dark mode as well as responsive behavior across mobile devices “Unlike traditional monitoring tools, the Circonus machine data intelligence platform was purpose-built to offer unlimited scalability,” said Bob Moul, CEO, Circonus. “It can ingest, store, and analyze trillions of measurements a second in real-time across billions of individual metric streams. This allows today’s enterprises to gain levels of valuable insights that were previously untapped, enabling them to improve the performance of their IT infrastructure, prevent issues before they arise, optimize processes, and make better business decisions.”

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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 ...

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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 ...

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Circonus Announces Availability of Spring 2020 Release

Circonus announced its Spring 2020 release. The highlight of the release is a Kubernetes monitoring solution that provides health-based alerting and horizontal pod auto-scaling. Additional enhancements include cloud monitoring, GCP Marketplace availability, performance improvements, and a more comprehensive Terraform integration. With these latest enhancements, Circonus becomes the only solution that can collect and analyze IT infrastructure, application, and container data at extreme scale across cloud, on-prem, and hybrid deployments — all in one platform. The Circonus Kubernetes monitoring solution lets enterprises monitor one or more Kubernetes clusters. It provides turnkey dashboards and alerting for increased visibility into cluster performance and health, and drives dynamic Kubernetes horizontal pod auto-scaling strategies that can be custom-tuned to the unique needs of an organization. Features include: - Easy-to-install agent - Immediate, real-time operational and health dashboards - Turnkey alerting for Kubernetes clusters to ensure Kubernetes remains healthy - Health-check insights such as crash loops, disk and memory pressure, job and volume failures, errors, pod pending delays, and deployment glitches - Native StatsD collection support - Horizontal pod auto-scaling What makes auto-scaling with Circonus unique is the ability to drive auto-scaling based on rich historical data analysis that customers have stored within Circonus — driving efficient, novel auto-scaling in a way that no other solution currently can. As a result, organizations can create dynamic auto-scaling strategies that will ensure optimal performance, reduce costs, and save time. Circonus’s cloud monitoring solution includes a cloud agent that provides a lightweight binary through which to easily collect metrics from Amazon Web Services (AWS), Azure, and Google Cloud Platform (GCP), as well as in-application dashboards. Customers can instantly move all of their metrics from multiple clouds into a single platform, enabling them to run queries, set alerts, and compare data from a single place. The Spring release also includes: - Performance improvements: Customers can now run analytic queries upon thousands of application metrics in near real time for unprecedented visibility into application performance - Comprehensive Terraform integration: Extension, documentation, and other improvements have been made to Circonus’s native Terraform provider - High availability metric ingestion: The Circonus cluster broker is designed to share the load of metric ingestion across multiple brokers, creating a fail-safe if one goes down - UI refresh: The UI has experienced a refresh with the addition of dark mode as well as responsive behavior across mobile devices “Unlike traditional monitoring tools, the Circonus machine data intelligence platform was purpose-built to offer unlimited scalability,” said Bob Moul, CEO, Circonus. “It can ingest, store, and analyze trillions of measurements a second in real-time across billions of individual metric streams. This allows today’s enterprises to gain levels of valuable insights that were previously untapped, enabling them to improve the performance of their IT infrastructure, prevent issues before they arise, optimize processes, and make better business decisions.”

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.