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Chronosphere Introduces Observability Data Optimization Cycle

Chronosphere is launching the Observability Data Optimization Cycle – a vendor-neutral framework to help companies regain control over observability data growth.

Chronosphere is also introducing new product features to support this framework, enabling teams to better understand and optimize the management of their cloud observability resources.

The Observability Data Optimization Cycle helps organizations to better understand and take action on the cost of their observability data through new features that support a process consisting of Analyzing, Refining and Operating:

- Centralized Governance: Provides engineering teams with broader authority to control data growth and predictability by enabling the Central Observability Team (COT) with information on how much data each team is using. It also assigns licensed capacity to individual teams so they can each prioritize based on their allotted amount of data.

- Usage Analyzer: Allows teams to view the cost and value of their data side by side, illustrating how and where the data is used, the volume of data used over a specific period of time, and which engineers are using the data.

- Shaping Policy UI: Helps teams preview the impact of shaping policies before implementing them so they can make adjustments when necessary.

- Derived Metrics: Makes metrics more straightforward by allowing organizations to store complex, high-value queries with more user-friendly names and visualizations.

"As more organizations adopt cloud native architectures, engineers are drowning in the massive amount of observability data that comes with it," said Martin Mao, CEO of Chronosphere. "This is causing an explosion in observability costs, while simultaneously overwhelming engineers in the troubleshooting process, leading to longer incidents and unhappy customers. Our new framework and features helps organizations achieve the best possible observability outcomes while keeping costs under control."

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Chronosphere Introduces Observability Data Optimization Cycle

Chronosphere is launching the Observability Data Optimization Cycle – a vendor-neutral framework to help companies regain control over observability data growth.

Chronosphere is also introducing new product features to support this framework, enabling teams to better understand and optimize the management of their cloud observability resources.

The Observability Data Optimization Cycle helps organizations to better understand and take action on the cost of their observability data through new features that support a process consisting of Analyzing, Refining and Operating:

- Centralized Governance: Provides engineering teams with broader authority to control data growth and predictability by enabling the Central Observability Team (COT) with information on how much data each team is using. It also assigns licensed capacity to individual teams so they can each prioritize based on their allotted amount of data.

- Usage Analyzer: Allows teams to view the cost and value of their data side by side, illustrating how and where the data is used, the volume of data used over a specific period of time, and which engineers are using the data.

- Shaping Policy UI: Helps teams preview the impact of shaping policies before implementing them so they can make adjustments when necessary.

- Derived Metrics: Makes metrics more straightforward by allowing organizations to store complex, high-value queries with more user-friendly names and visualizations.

"As more organizations adopt cloud native architectures, engineers are drowning in the massive amount of observability data that comes with it," said Martin Mao, CEO of Chronosphere. "This is causing an explosion in observability costs, while simultaneously overwhelming engineers in the troubleshooting process, leading to longer incidents and unhappy customers. Our new framework and features helps organizations achieve the best possible observability outcomes while keeping costs under control."

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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