What’s new in Grafana Cloud

By leveraging structured metadata under the hood, Cloud Logs now provides a simplified syntax and better query performance for logs sent through our Cloud OTLP endpoint.

You now have the ability to customize specific colors for individual thresholds when using the Config from query results transformer. Previously, when you added multiple thresholds, they all defaulted to the same color, red. With this addition, you gain the flexibility to assign distinct colors to each threshold. This feature addresses a common pain point highlighted by users. With customizable threshold colors, you now have greater control over your data representation, fostering more insightful and impactful analyses across diverse datasets.

You can now configure the Azure Monitor data source to authenticate as the logged-in Grafana user when making query and resource requests if you also use Azure Entra to sign your users into Grafana. Current User authentication allows you to enforce Azure RBAC restrictions on your Grafana users by removing the need to provide broad service credentials. Once a data source is configured with Current User authentication a user will only have access to resources they can access directly in Azure.

We’ve made a number of improvements to the canvas visualization. Enhanced flowcharting functionality With this release, we’ve updated the canvas visualization to include much-requested flowcharting features. These improvements are: Addition of widely-used elements: cloud, parallelogram, and triangle. Addition of midpoint controls so that the connectors no longer have to be straight lines. Addition of more connector styles including dashed lines as well as corner radius and direction control. Horizontal and vertical snapping for connectors.

With the newly added Infinite panning editor option, you can now view and navigate very large canvases. This option is displayed when the Pan and zoom switch is enabled. To try out this feature, you must first enable the canvasPanelPanZoom feature toggle.

The new alert rule detail view has a new look and feel with helpful metadata at the top. The namespace and group are shown in the breadcrumb navigation. This is interactive and can be used to filter rules by namespace or group. The rest of the alert detail content is split up into tabs: Query and conditions View the details of the query that is used for the alert rule, including the expressions and intermediate values for each step of the expression pipeline.

We released generative AI support in both OSS and Cloud last year, supporting features like Incident auto-summary and dashboard creation assistance. One downside was that it was tedious to set up – you had to install & enable the LLM plugin, and separately sign up with OpenAI or Azure to configure your own API keys. On Cloud, this just got a lot easier! In your Grafana Cloud account, you’ll find the LLM plugin already there and ready to go.

The Grafana plugins catalog contains over 200 data sources, panels and applications to help you make the most of your data. However, finding the right plugin can sometimes be a challenge. Our initial search capability required an exact match on the name of the given plugin. With our latest update, the new fuzzy search algorithm lets you combine ID, name, plugin type, organization name, and keywords in one input, making it more flexible and accurate.

Removal of old Tempo Search tab In Grafana v10.1, we added a Tempo search editor powered by TraceQL (search tab). We also recommended using this new editor over the older non-TraceQL powered editor. The older non-TraceQL powered editor has been removed. Any existing queries using the older editor will be automatically migrated to the new TraceQL-powered editor. The new TraceQL-powered editor makes it much easier to build your query by way of static filters, better input/selection validation, copy query to the TraceQL tab, query preview, dedicated status filter, and the ability to run aggregate by (metrics summary) queries.

(Re-)introducing “Keep Last State” to Grafana managed alert rules. You can now choose to keep the last evaluated state of an alert rule when that rule produces “No Data” or “Error” results. Simply choose the “Keep Last State” option for no data or error handling when editing a rule. Refer to the Alerting documentation on state and health of alert rules for more information.

We are happy to announce Cloud Logs Export is now Generally Available to all Grafana Cloud users! Cloud Logs Export lets users ship logs to their own object storage (AWS, GCP, Azure) for low-cost, long term retention. If you need to retain logs for long periods of time, without a need to frequently query them, Cloud Logs is the most cost effective way to do that. To learn more, read our public preview blog post announcement, or try it out yourself (From the Grafana menu, “Plugins” -> “Cloud Logs Exporter” to get started!

Dashboards, when accessed by users with the Viewer role, are now using the Scenes library. Those users shouldn’t see any difference in the dashboards apart from two small changes to the user interface (UI): the variables UI has slightly changed and the time picker is now part of the dashboard container. Dashboards aren’t affected for users in other roles. This is the first step towards a more robust and dynamic dashboarding system that we’ll be releasing in the upcoming months.

Directly from Kubernetes Monitoring, you can click into the Asserts RCA Workbench to see what is occurring between components and their relationships. (Release 2.0.4)

You can now use generative AI to assist you in your Grafana dashboards. So far generative AI can help you generate panel and dashboard titles and descriptions - You can now generate a title and description for your panel or dashboard based on the data you’ve added to it. This is useful when you want to quickly visualize your data and don’t want to spend time coming up with a title or description.

Flamegraph AI in Grafana Cloud Profiles is now GA Flamegraph AI helps you understand your profiling data by using a large-language model (LLM) to assist with data interpretation. A flamegraph provides a convenient way to visualize performance data. These graphs provide a clear, intuitive understanding of resource allocation and bottlenecks within an application. Flamegraphs and profiling data in general can help you understand: Performance bottlenecks: What’s causing the slowdown? Root causes: Why is it happening?