Chronosphere announced the availability of Chronosphere Lens, a new way to interact with metrics, traces, and events in a single, automatically generated, service-oriented view.
The company also announced Change Event Tracking and other features that empower developers with additional context and clarity. Collectively, these features are designed to help bring observability data into focus.
Chronosphere Lens brings familiar service-oriented observability principles to the cloud native era. Its main goal is to speak the language of developers, who today must keep track of not just how their system is architected and operated, but also how to use the observability tools that measure performance and reliability.
- By automatically generating and maintaining service-oriented views based on real-time cloud native telemetry streams, Chronosphere Lens offers a single, up-to-date, and consistent perspective of system health.
- The unified approach and seamless correlation of metrics, traces and events allows teams to pinpoint and resolve issues more efficiently, eliminating the cognitive dissonance often experienced with traditional tools, and ultimately reducing downtime and operational overhead.
- With less setup hassle, Chronosphere Lens enables engineering teams to focus more on what truly matters—building innovative, revenue-generating features that address customer needs.
Change Event Tracking is a set of features within Chronosphere that gives developers instant insight into what changes introduced problems across their infrastructure, applications or business, via integrations with various DevOps and PaaS tools.
- Chronosphere centralizes and correlates this information in the context of metrics and traces, offering a cohesive view and actionable information.
- Improved context around changes and anomalies directly contributes to faster problem-solving and improved developer productivity, freeing up resources for more innovative tasks.
“Chronosphere Lens simplifies observability for developers and ultimately helps engineering teams save time and reduce costs,” said Martin Mao, CEO and Founder, Chronosphere. “On top of building tools that are faster, more reliable, and more scalable, we also want to reduce operational complexity for our customers. Saving end users time, effort, and cognitive overhead is just as important to us as serving faster queries and providing industry-leading uptime.”
The Latest
80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...
40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...
Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...
Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...
Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...
Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...
Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...
Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...
Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...
If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...