Grafana Labs announced new features tailored for Kubernetes platform teams using Grafana Cloud.
Kubernetes Monitoring is a solution for proactive monitoring and troubleshooting within the fully managed Grafana Cloud observability platform. It provides a single pane of glass across your Kubernetes infrastructure, offering out-of-the-box visualizations of user data, cost monitoring insights, preconfigured alerts, alert rules, recording rules, and AI/ML predictions to simplify tasks that have been historically complex and time-consuming to achieve with just kubectl.
The solution’s real-time visibility, alerting, and troubleshooting capabilities for Kubernetes environments help organizations like Beeswax efficiently manage and optimize their container-based infrastructure. James Wojewoda, Lead Site Reliability Engineer at Beeswax, said, “Kubernetes Monitoring on Grafana Cloud enables our engineers to have native monitoring. No longer do they have to reach out to our SRE team. Instead, they just click a button on the Grafana Cloud integrations tab, navigate to the out-of-the-box dashboard, and see all the information — CPU usage, logs, metrics — they need to solve the problem themselves. It’s so simple, helps us spot issues fast, and saves us all a lot of custom development time.”
The latest updates include:
- Contextual root cause analysis for Kubernetes-based applications: At ObservabilityCON in September, Grafana Labs announced a suite of contextual root cause analysis workflows in Grafana Cloud. These workflows leverage AI/ML to automate root cause analysis in users’ Kubernetes environments, simplifying troubleshooting and reducing MTTR. Historically, diagnosing issues in complex Kubernetes-based applications has been a time-consuming task, requiring manual correlation of data from both application and infrastructure layers. By automatically identifying and correlating anomalies across these layers, this technology provides teams with contextualized insights, enabling faster and more accurate issue resolution.
- Availability in AWS Marketplace: Kubernetes Monitoring is now available in AWS Marketplace. Now, AWS users can easily deploy the solution with a single step in the AWS console or through the AWS Command Line Interface.
- Improved operational monitoring with Sift investigations: Kubernetes Monitoring also streamlines operational monitoring with Sift investigations by instantly identifying critical deployment issues. The platform’s intelligent alerting system automatically detects deployments with insufficient ready replicas and quickly pinpoints problematic deployments and pods requiring restart. This enhanced visibility not only reduces MTTR but also significantly improves overall system reliability.
- Enhanced historical visibility: While traditional kubectl commands offer limited historical data retention and require manual intervention, Kubernetes Monitoring in Grafana Cloud provides automatic, comprehensive historical tracking of pods, nodes, and clusters—even after deletion or recreation. This enables SREs to efficiently troubleshoot post-deployment issues, optimize resource allocation, and conduct thorough incident analyses without the complexity of maintaining additional infrastructure or orchestrating multiple tools.
- Troubleshooting enhancements: Kubernetes Monitoring has added more troubleshooting tools to help users easily find deleted objects, zoom in on a graph to narrow a time range for more analysis, and jump directly to Clusters, Nodes, and workloads directly from the homepage.
- New views: One of the most popular features within Kubernetes Monitoring, cost monitoring, now provides a 90-day view of total compute cost, average cost per Pod, and average Pod count in the Cost overview tab. In addition, Kubernetes Monitoring now allows users to view energy data for their Kubernetes infrastructure components.
In addition, Grafana Labs continues to lean into its big tent philosophy while strengthening its commitment to the open source Kubernetes community by leading the development of the Kubernetes Monitoring Helm chart—a powerful open source solution for collecting comprehensive telemetry data from Kubernetes clusters. The chart enables the collection of metrics, logs, traces, and profiles, with capabilities for local processing and flexible data routing to the backend database of your choosing. While optimized for Grafana Cloud, it integrates seamlessly with open source databases including Mimir, Loki, Tempo, Pyroscope, and more. The upcoming 2.0 release will bring many improvements including the ability to send data to multiple destinations, built-in service integrations, and a simplified configuration experience.
The Latest
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 ...
According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...
2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...
Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...