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Chronosphere Adds Tracing Capabilities

Chronosphere has added tracing capabilities to its platform for monitoring today’s complex technology stacks at large scale.

This allows users to correlate traces with metrics, adding contextual information to alerts and dashboards in order to reduce the amount of time it takes to resolve production issues.

The new feature is based on open source and CNCF technologies including OpenTelemetry, OpenMetrics and M3, all three of which Chronosphere contributes to.

Chronosphere, founded by former Uber engineers who created M3, formally launched earlier this month. Dennis Drogseth, VP at Enterprise Management Associates (EMA), said: "Chronosphere, born out of demanding real-world requirements, is a breath of fresh air with its fast time to value and proven capabilities in helping large, enterprise IT organizations proactively address complex service delivery problems across multi-cloud and hybrid cloud environments.”

Many of today’s monitoring platforms offer both metrics and tracing capabilities and claim tight integrations between the two observability pillars. However diving into these integrations, it becomes apparent that they are two separate systems that are loosely linked via a common period of time and a common set of attributes and labels. While this does help narrow down the search space, manual intervention is often required to link and make sense of the two types of monitoring data.

The problem with manual intervention is that each minute the issue remains unresolved, the business is negatively impacted and due to this, engineers are often under immense pressure to find the cause of issues quickly. An ideal solution would present all the context of the issue upfront, reducing the time it takes to identify and mitigate issues - and that is what Chronosphere’s monitoring platform offers.

Chronosphere’s solution to this problem is to deeply link the traces and metrics from the moment they are emitted. The trace identifiers are stored right next to the metric values in Chronosphere’s metrics storage system M3. When a user receives an alert or views their dashboard, the very traces that constituted those metric values are displayed instantly. This saves valuable minutes during an outage and helps users identify and mitigate issues much faster.

“The best part of our solution is that we can achieve this deep linking in a cost-efficient way,” said Rob Skillington, co-founder and CTO of Chronosphere. “This would be an easy problem to solve if you simply stored every trace, but in most cases, that’s way too expensive to do at any real level of scale.”

Chronosphere achieves cost efficiency by leveraging their existing metrics aggregation tier to selectively store unique traces. This allows users to both find that needle in a haystack - the one in a thousand error or slow request - while using a fraction of the storage.

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Chronosphere Adds Tracing Capabilities

Chronosphere has added tracing capabilities to its platform for monitoring today’s complex technology stacks at large scale.

This allows users to correlate traces with metrics, adding contextual information to alerts and dashboards in order to reduce the amount of time it takes to resolve production issues.

The new feature is based on open source and CNCF technologies including OpenTelemetry, OpenMetrics and M3, all three of which Chronosphere contributes to.

Chronosphere, founded by former Uber engineers who created M3, formally launched earlier this month. Dennis Drogseth, VP at Enterprise Management Associates (EMA), said: "Chronosphere, born out of demanding real-world requirements, is a breath of fresh air with its fast time to value and proven capabilities in helping large, enterprise IT organizations proactively address complex service delivery problems across multi-cloud and hybrid cloud environments.”

Many of today’s monitoring platforms offer both metrics and tracing capabilities and claim tight integrations between the two observability pillars. However diving into these integrations, it becomes apparent that they are two separate systems that are loosely linked via a common period of time and a common set of attributes and labels. While this does help narrow down the search space, manual intervention is often required to link and make sense of the two types of monitoring data.

The problem with manual intervention is that each minute the issue remains unresolved, the business is negatively impacted and due to this, engineers are often under immense pressure to find the cause of issues quickly. An ideal solution would present all the context of the issue upfront, reducing the time it takes to identify and mitigate issues - and that is what Chronosphere’s monitoring platform offers.

Chronosphere’s solution to this problem is to deeply link the traces and metrics from the moment they are emitted. The trace identifiers are stored right next to the metric values in Chronosphere’s metrics storage system M3. When a user receives an alert or views their dashboard, the very traces that constituted those metric values are displayed instantly. This saves valuable minutes during an outage and helps users identify and mitigate issues much faster.

“The best part of our solution is that we can achieve this deep linking in a cost-efficient way,” said Rob Skillington, co-founder and CTO of Chronosphere. “This would be an easy problem to solve if you simply stored every trace, but in most cases, that’s way too expensive to do at any real level of scale.”

Chronosphere achieves cost efficiency by leveraging their existing metrics aggregation tier to selectively store unique traces. This allows users to both find that needle in a haystack - the one in a thousand error or slow request - while using a fraction of the storage.

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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 ...