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
Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...
Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...
Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...
The more technology businesses invest in, the more potential attack surfaces they have that can be exploited. Without the right continuity plans in place, the disruptions caused by these attacks can bring operations to a standstill and cause irreparable damage to an organization. It's essential to take the time now to ensure your business has the right tools, processes, and recovery initiatives in place to weather any type of IT disaster that comes up. Here are some effective strategies you can follow to achieve this ...
In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI ...
In MEAN TIME TO INSIGHT Episode 19, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA explains the cause of the AWS outage in October ...
The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before. But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck ...
On September 16, the world celebrated the 10th annual IT Pro Day, giving companies a chance to laud the professionals who serve as the backbone to almost every successful business across the globe. Despite the growing importance of their roles, many IT pros still work in the background and often go underappreciated ...
Artificial Intelligence (AI) is reshaping observability, and observability is becoming essential for AI. This is a two-way relationship that is increasingly relevant as enterprises scale generative AI ... This dual role makes AI and observability inseparable. In this blog, I cover more details of each side ...
Poor DEX directly costs global businesses an average of 470,000 hours per year, equivalent to around 226 full-time employees, according to a new report from Nexthink, Cracking the DEX Equation: The Annual Workplace Productivity Report. This indicates that digital friction is a vital and underreported element of the global productivity crisis ...