Skip to main content

Circonus Platform Released

Circonus announced the launch and availability of its Circonus Platform, a machine data intelligence platform built to provide enterprises with the ability to handle the rapidly increasing data volume and frequency seen in today's global IT infrastructure and IoT deployments. With the Circonus Platform, enterprises can collect, store and analyze all of their machine data – without limits – ushering in a new era of extraordinary business insight and intelligence. Machine data intelligence is the ability to gather and analyze vast amounts of machine-generated data, including data from sensors, systems, and connected devices, to achieve new levels of insight that drive smarter operations and better decision-making. Machine data is becoming increasingly valuable to enterprises. As the number of connected devices multiplies and data production costs plummet, it is unleashing data volumes that can overwhelm an enterprise, but that can also create an entirely new source of competitive advantage if harnessed. The ability to tap into this ocean of data, monitor and analyze it in real time, and collect and store it in such a way that it can be mined at will is at the heart of machine data intelligence. Analyzing high-volume, high-frequency data is a challenge for many enterprises, and one that is only getting more ominous. IDC estimates that there will be 41.6 billion connected IoT devices, or "things," generating 79.4 zettabytes (ZB) of data in 2025. The growing value of accurate, real-time machine data intelligence is also illustrated by core business systems that need to be running 24x7x365, with the cost of infrastructure downtime averaging $5,600 per minute, and as high as $540,000 per hour, according to Gartner. The Circonus Platform has the ability to cost-effectively handle the data volume, frequency, speed, and accuracy required of today's –and tomorrow's – mission-critical IoT and monitoring systems. "Circonus is changing the way that enterprises think about their machine data and its potential. For the first time, organizations can tap into the full value of all of their machine data, from servers to robots, without constraint or compromise. We believe that machine data intelligence will fuel the next wave of competitive differentiation and business value. Companies that take full advantage of this data to optimize operations, innovate product and service offerings, and create new revenue streams will be the clear winners in the 'Internet of Everything' economy," said Bob Moul, CEO of Circonus. Built on the proven and performant Circonus IRONdb time-series database, the Circonus Platform is designed to meet the requirements of the most demanding use cases in consumer and Industrial Internet of Things (IoT and IIoT), as well as operational analytics and infrastructure monitoring across a number of vertical markets. Key features of the Circonus Platform include: - High ingestion rates - Unlimited scale and volume - Millisecond frequency - Accurate, real-time analytics - Ability to work with any technology - Unlimited retention and data access "The Circonus Platform is second to none in its ability to collect, store, manage and analyze machine data at scale," said Theo Schlossnagle, Founder and CTO of Circonus. "We have created the first machine data intelligence platform that can handle the scale that comes from high-frequency data collection from billions of devices without losing speed, accuracy, or cost efficiencies. This is the future."

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.

Circonus Platform Released

Circonus announced the launch and availability of its Circonus Platform, a machine data intelligence platform built to provide enterprises with the ability to handle the rapidly increasing data volume and frequency seen in today's global IT infrastructure and IoT deployments. With the Circonus Platform, enterprises can collect, store and analyze all of their machine data – without limits – ushering in a new era of extraordinary business insight and intelligence. Machine data intelligence is the ability to gather and analyze vast amounts of machine-generated data, including data from sensors, systems, and connected devices, to achieve new levels of insight that drive smarter operations and better decision-making. Machine data is becoming increasingly valuable to enterprises. As the number of connected devices multiplies and data production costs plummet, it is unleashing data volumes that can overwhelm an enterprise, but that can also create an entirely new source of competitive advantage if harnessed. The ability to tap into this ocean of data, monitor and analyze it in real time, and collect and store it in such a way that it can be mined at will is at the heart of machine data intelligence. Analyzing high-volume, high-frequency data is a challenge for many enterprises, and one that is only getting more ominous. IDC estimates that there will be 41.6 billion connected IoT devices, or "things," generating 79.4 zettabytes (ZB) of data in 2025. The growing value of accurate, real-time machine data intelligence is also illustrated by core business systems that need to be running 24x7x365, with the cost of infrastructure downtime averaging $5,600 per minute, and as high as $540,000 per hour, according to Gartner. The Circonus Platform has the ability to cost-effectively handle the data volume, frequency, speed, and accuracy required of today's –and tomorrow's – mission-critical IoT and monitoring systems. "Circonus is changing the way that enterprises think about their machine data and its potential. For the first time, organizations can tap into the full value of all of their machine data, from servers to robots, without constraint or compromise. We believe that machine data intelligence will fuel the next wave of competitive differentiation and business value. Companies that take full advantage of this data to optimize operations, innovate product and service offerings, and create new revenue streams will be the clear winners in the 'Internet of Everything' economy," said Bob Moul, CEO of Circonus. Built on the proven and performant Circonus IRONdb time-series database, the Circonus Platform is designed to meet the requirements of the most demanding use cases in consumer and Industrial Internet of Things (IoT and IIoT), as well as operational analytics and infrastructure monitoring across a number of vertical markets. Key features of the Circonus Platform include: - High ingestion rates - Unlimited scale and volume - Millisecond frequency - Accurate, real-time analytics - Ability to work with any technology - Unlimited retention and data access "The Circonus Platform is second to none in its ability to collect, store, manage and analyze machine data at scale," said Theo Schlossnagle, Founder and CTO of Circonus. "We have created the first machine data intelligence platform that can handle the scale that comes from high-frequency data collection from billions of devices without losing speed, accuracy, or cost efficiencies. This is the future."

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.