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
In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability...
While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...