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Chronosphere Launches New Release of Cloud Native Observability Platform

Chronosphere launched a new release of its cloud native observability platform that includes new capabilities designed to improve cloud native engineering team efficiency by streamlining workflows and speeding up mean time to detection and remediation (MTTD) (MTTR).

The Chronosphere platform takes a new approach to cloud native observability with a reimagined user workflow tailored to the unique ways engineering and DevOps teams work in today's cloud native environment. Chronosphere's platform gives customers the tools they need to organize their teams, users, and observability data in order to speed up MTTD and MTTR making engineers' lives easier and increasing overall productivity.

"Great observability is not about having more data — its about having the right data, in the right context at the right time." said Martin Mao, Co-founder and CEO of Chronosphere. "The new release of Chronosphere was designed to work alongside engineers, enabling them to prioritize the data that is most important to them. All of the capabilities built into our platform, from trace metrics to collections and workspace dashboards, lead back to our mission of increasing the productivity of engineering teams and in turn, reducing burnout "

The new release will be available to all Chronosphere customers and includes the following new capabilities:

- Collections and Workspaces - A streamlined workflow that presents the right data in the right context so teams can troubleshoot faster. Too often issue resolution takes too long and relies on institutional knowledge and power users. With Workspaces, users have a global view of all data but can easily zoom in on the data most relevant to the services for which they are responsible.

- Query Accelerator - Automatically and continuously scans for slow dashboard queries and augments them with their faster alternative. This capability eliminates the need for engineers to be proficient at writing "good queries." They can create a query that returns the data they need, and Query Accelerator will ensure that it performs optimally on every dashboard where it is used.

- Quotas - Provides teams with an easy way to allocate licensed data capacity amongst teams and services. Quotas gives engineering team leaders a deeper understanding of their data — from its usage to the impact of changes — helping them make better decisions on what data to protect or sacrifice.

- Trace metrics - Customers can leverage trace data to define metrics and alerts. This gives users the ability to quickly jump from a trace metric alert to the associated trace data — a powerful tool in the triage process to find where a new error or latency exists, ultimately speeding up remediation times and improving system efficiency.

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Chronosphere Launches New Release of Cloud Native Observability Platform

Chronosphere launched a new release of its cloud native observability platform that includes new capabilities designed to improve cloud native engineering team efficiency by streamlining workflows and speeding up mean time to detection and remediation (MTTD) (MTTR).

The Chronosphere platform takes a new approach to cloud native observability with a reimagined user workflow tailored to the unique ways engineering and DevOps teams work in today's cloud native environment. Chronosphere's platform gives customers the tools they need to organize their teams, users, and observability data in order to speed up MTTD and MTTR making engineers' lives easier and increasing overall productivity.

"Great observability is not about having more data — its about having the right data, in the right context at the right time." said Martin Mao, Co-founder and CEO of Chronosphere. "The new release of Chronosphere was designed to work alongside engineers, enabling them to prioritize the data that is most important to them. All of the capabilities built into our platform, from trace metrics to collections and workspace dashboards, lead back to our mission of increasing the productivity of engineering teams and in turn, reducing burnout "

The new release will be available to all Chronosphere customers and includes the following new capabilities:

- Collections and Workspaces - A streamlined workflow that presents the right data in the right context so teams can troubleshoot faster. Too often issue resolution takes too long and relies on institutional knowledge and power users. With Workspaces, users have a global view of all data but can easily zoom in on the data most relevant to the services for which they are responsible.

- Query Accelerator - Automatically and continuously scans for slow dashboard queries and augments them with their faster alternative. This capability eliminates the need for engineers to be proficient at writing "good queries." They can create a query that returns the data they need, and Query Accelerator will ensure that it performs optimally on every dashboard where it is used.

- Quotas - Provides teams with an easy way to allocate licensed data capacity amongst teams and services. Quotas gives engineering team leaders a deeper understanding of their data — from its usage to the impact of changes — helping them make better decisions on what data to protect or sacrifice.

- Trace metrics - Customers can leverage trace data to define metrics and alerts. This gives users the ability to quickly jump from a trace metric alert to the associated trace data — a powerful tool in the triage process to find where a new error or latency exists, ultimately speeding up remediation times and improving system efficiency.

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...