
Grafana Labs announced updates to Kubernetes Monitoring in Grafana Cloud, the solution for all levels of Kubernetes usage within an organization.
These new features target resource utilization and predictions, historical state analysis, and simplified third-party integrations.
With Kubernetes Monitoring, the solution introduced to the fully managed Grafana Cloud platform last year, users can automatically ship metrics to Grafana after installing the Grafana Agent into one or more Kubernetes clusters. Once this connection is made, Grafana Cloud users have out-of-the-box access to their Kubernetes metrics, logs, and events via pre-built dashboards and alerts.
Grafana Labs continues to expand the solution's capabilities to give users even more visibility into their Kubernetes fleet:
- Visualize resource utilization efficiency: With this new cost-focused feature, users get a bird's eye view of all the nodes in a cluster and the condition of those nodes. This helps reduce the deviation between resource allocation and actual resource utilization. Read more in this blog post.
- Predict CPU and RAM usage: This new ML-powered feature provides resource usage forecasts with a simple click. It helps users assure high availability and avoid performance degradation. With insights of future resource usage peaks, users can identify resource deprivation issues in their Kubernetes infrastructure. This feature is currently available for Grafana Cloud Pro and Advanced users.
- Retroactively visualize the state of your fleet: Kubernetes Monitoring visualizes a fleet's historical state in respect to user-selected dates and time. Users can navigate to different time frames to see the exact state of all visualizations during that moment. This feature will be available by the end of April.
- Monitor additional services running on Kubernetes: With the recently released Kubernetes Monitoring integrations, users can now monitor additional services running on Kubernetes with no more effort than selecting which service to connect to in the Grafana Connections UI. Current integrations include Grafana Mimir, CockroachDB, cert-manager, and Node Exporter, with NGINX, CoreDNS, and etcd coming soon.
Kubernetes Monitoring is available to all Grafana Cloud users, including those on the generous, forever-free tier. For more information on getting started with Kubernetes Monitoring in Grafana Cloud, visit the Kubernetes Monitoring solutions page, read our Kubernetes Monitoring blogs, check out our Kubernetes Monitoring documentation, and watch our webinars: Kubernetes monitoring, out-of-the-box with Grafana Cloud and How to control metrics growth in Prometheus and Kubernetes with Grafana Cloud.
In addition, Grafana Incident is a tool available in Grafana Cloud that automates the toilsome tasks of incident management. Its new Investigations feature streamlines incident response in Kubernetes environments by automatically identifying issues in cluster services, such as noisy neighbors and increased log errors, upon incident declaration from an alert. This functionality will soon be available in preview for Grafana Cloud Pro and Advanced users.
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 ...