Grafana Labs announced the release of Grafana 11.0.
The 11.0 release of Grafana, the company’s flagship open source data visualization platform, includes new updates that make it easier and faster to connect users’ data, visualize it in a beautiful and functional way, share it with others, and respond to incidents.
This release includes a host of improvements and new features that help all users – from SREs to hobbyists – keep their systems healthy with flexible data visualization and allow large organizations to level up their centralized observability strategy, including:
- Faster Root Cause Analysis with Explore Metrics: Explore Metrics provides a query-less experience for Prometheus metrics. Without so much as seeing a PromQL query, users can now visualize a metric, drill down to spot anomalies, and pivot to similar metrics with the same labels to discover the root cause of issues and get a holistic view of their data. The team also introduced Explore Logs, which provides a query-less experience for browsing Loki logs. Users can view logs by service as well as navigate and filter logs based on volume, labels, and patterns – all without LogQL. It will be available in preview for users of Grafana 11+ and Loki 3.0.
- Improved Visualizations: The new Scenes-based architecture introduces an edit mode for dashboards as well as a more consistent sharing experience. The new logs table view makes logs easier to view, read, and parse in Explore. The Canvas panel now has pan and zoom, snap and alignment, and buttons to interact with the systems users are monitoring.
- Simpler Alerting: New Grafana Alerting functionality includes connecting alert rules directly to contact points, improved Terraform management, and finer-grained access control.
- Expanding Grafana’s Big Tent with New Data Sources: Grafana 11 introduces new data sources – Falcon LogScale, Looker, Pagerduty, and SumoLogic – with many more shipping by the end of the year.
- Integrate with Tempo and Traces: Users will now be able to see profiles within a trace span to figure out not just what process, but also what function, took all the time in a given request.
- Configure SSO in the UI: Administrators can set up Oauth or SAML user authentication and synchronize teams to Grafana with a new UI.
- Build observability applications with the New Grafana App Platform: The new Grafana App Platform extends the core Grafana API to provide stronger integration points for application developers. Developers can get started in our new dev portal.
Grafana 11 is currently available in public preview and is rolling out to Grafana Cloud users. It will be generally available on May 14.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.