What's new from Grafana Labs

Save money and apply an extra layer of network security by using GCP Private Service Connect to send telemetry data to Grafana Cloud.

Normally when you send telemetry from your GCP network to Grafana Cloud, you incur in network egress fees and your data, though encrypted, traverses the public internet.

We’re excited to announce the General Availability of the Logs Drilldown JSON viewer in Grafana v11.6 and later! 🚀

The Logs Drilldown JSON viewer provides you a log line viewer specifically created to make it easier to read and understand your JSON log data.

With this release, we’re rethinking our dashboard APIs and making the default a new model that is consistent, versioned, and resource-oriented. 

We started the rollout in May. Since then, we’ve gathered feedback, measured impact, and improved testing. We’re now confident the APIs are stable and have made them the default.

We’re excited to announce the general availability of new alerting capabilities for Synthetic Monitoring. These new capabilities are available to all Grafana Cloud users.

You can now create alerts for each check in your Synthetic Monitoring application. For example, you can create an alert based on the number of check failures in a specific time window, with different settings for each one of your checks.

We are excited to announce the general availability of Amazon CloudWatch metric streams.

CloudWatch metric streams with Amazon Data Firehose offer you a less expensive, highly accurate way to import metrics from AWS into Grafana Cloud. As with CloudWatch metrics scrape, CloudWatch metric streams do not require you to deploy or maintain Alloy agents or servers and can be configured as code using Terraform or using a CloudFormation template.

You can now control the direction of connections between canvas elements by mapping the direction to a field value. A positive value draws the connection arrow forward, a negative value draws it in reverse, and a value of zero removes the directional arrow entirely. Alternatively, you can assign a fixed direction value.

This is especially useful for visualizing real-time data flows, system states, or transitions, where directionality changes based on metrics.

The Tooltip from field option in table visualizations allows you to use the values from another field (or column) in a tooltip.

When you toggle the switch on, you can select from a drop-down list any of the fields in the table to be used as the source of the tooltip content. All table fields are available for selection, whether visible or hidden.

We are excited to announce the general availability of k6 extensions in Grafana Cloud k6.

You can now seamlessly use extensions from both Grafana Cloud and the k6 CLI:

• Local execution: k6 run test.js
• Cloud execution: k6 cloud run test.js
• Local execution, streaming test results: k6 cloud run test.js --local-execution

Grafana Assistant, Grafana’s AI tool within Grafana, is now available in Public Preview.

Grafana Assistant is an AI tool that can…

• … create dashboards.
• … write queries.
• … help you get onboarded.
• … support you in learning Grafana.
• … support in investigations and incidents.

The Jenkins data source allows you to query and visualize metrics such as projects, builds, build queues, nodes and load statistics from within Grafana.

This enterprise plugin includes two built-in dashboards to help you quickly get started visualizing Jenkins data:

• Jenkins Overview dashboard provides an overview of the Jenkins instance, including all projects, nodes, executor status and build queue.
• Jenkins DORA Metrics dashboard provides information on four key metrics used to assess software development team performance: deployment frequency, lead time for changes, change failure rate and time to restore service. These metrics focus on both the speed and stability of software delivery.

We’re excited to announce the public preview release of secrets management for Synthetic Monitoring, available to all Grafana Cloud users.

Secrets management gives you a centralized place to securely store sensitive data like API keys, passwords, and tokens.

We’re excited to announce the integration of the Model Context Protocol (MCP) into Grafana Cloud Traces and into open-source Tempo (merged, available in Tempo 2.9). MCP, a standard developed by Anthropic, allows data sources to expose data and functionality to Large Language Models (LLMs) via an agent.

This integration opens up new possibilities for interacting with tracing data. You can now connect LLM-powered tools like Claude Code or Cursor to Grafana Cloud Traces, enabling you to:

• Explore services and understand interactions: LLMs can be used to teach new developers about service interactions within an application by analyzing tracing data. For instance, a new developer could ask the AI to explain how their services interact. The AI would use live tracing data from Cloud Traces to answer these questions.
• Diagnose and investigate errors: You can leverage LLMs to identify and diagnose errors in your systems. The AI can answer questions like “Are there errors in my services?”, “What endpoints are being impacted”, etc?
• Optimize performance and reduce latency: LLMs can assist in identifying the causes of latency and guiding optimization efforts. By analyzing trace data, an LLM can summarize operations in a request path, pinpoint bottlenecks, and even suggest code changes to improve performance, such as parallelizing operations.

Grafana IRM outgoing webhooks now support incident events, providing a unified experience for automating alert group and incident workflows. You can configure webhooks from the Outgoing Webhooks tab and trigger requests based on key incident lifecycle events, such as when an incident is declared, updated, or resolved.

With this update, you can:

• Customize webhook requests with support for any HTTP method
• Use templated URLs, headers, and request bodies
• Dynamically reference incident data and prior webhook responses
• View webhook execution details directly from the incident or alert group timeline
• Manage incident-related webhook configurations using Terraform

Creating effective alert rules often requires knowing exactly what query to write upfront. But sometimes you need to explore your data first — understanding how metrics behave across different label combinations, spotting patterns, and building confidence in your monitoring approach before committing to an alert rule.

The Solution:

Metrics Drilldown integration brings visual data exploration directly into alert creation. Launch from your alert rule editor into Grafana’s queryless Metrics Drilldown experience, where you can “quickly find related metrics with just a few simple clicks, without needing to write a PromQL query.” Explore your Prometheus data through intuitive point-and-click interactions, visualize how metrics break down across labels, and leverage “smart segmentation” with “optimal chart types selected automatically.” When you discover the perfect visualization, click through to Explore to grab the underlying query and use it in your alert rule.

What Makes This Special:

We’re excited to share a new integration between Tailscale and Grafana Cloud that lets you query data sources on your Tailscale network directly from your Grafana Cloud stack.

Tailscale allows you to create a secure network (called a tailnet) by directly connecting users, devices, and resources. This new integration adds an ephemeral machine to your tailnet on your behalf. You can add tags to these machines, which allow you to configure Tailscale ACLs and Grants, giving you full control of what your Grafana Cloud stack can access.