Tailoring Cloud Observability: How to Customize Prebuilt Dashboards for AWS, Azure, and GCP in Grafana

Grafana Cloud's Cloud Provider Observability comes with ready-to-use dashboards for AWS, Azure, and Google Cloud. These out-of-the-box views provide service overviews, instance-level details, and quick links to dive into your data. But what if you already have a trusted dashboard, need a view that matches your team's workflow, or want to change the panels shown when drilling into a single instance? The good news: you can now customize all of that directly within the app—without building anything from scratch. This Q&A guide walks through the three main ways to personalize service views: connecting an existing dashboard, creating one with AI, and editing the instance drill-down panels that appear across multiple observability surfaces.

What customization options does Cloud Provider Observability offer?

Cloud Provider Observability gives you three powerful customization capabilities. First, quick links and default dashboard: you can replace the built-in default view for any cloud service (like Amazon RDS or Azure VMs) with a custom dashboard of your choice, and add extra dashboards as quick links. Second, instance drill-down: the panels and queries you configure for an instance-level view will render consistently everywhere that view appears—whether in Cloud Provider Observability, Database Observability, or the entity graph. Third, AI-generated dashboards: you can create new dashboards using Grafana's AI assistant, wire them in with the correct variables, and set them as defaults. Together, these options let you keep using the premade views where they fit, plug in your own dashboards where you want a different "front door," and tailor the per-instance drill-down for a uniform experience across all observability surfaces.

How do I connect an existing dashboard to a cloud service?

If you already have a trusted dashboard—say, your internal RDS monitoring or Lambda performance view—you can attach it as a quick link to the corresponding cloud service. Optionally, you can even make it the default view so users see it first when opening that service from the services tab, entity graph, or other entry points. To do this, navigate to the Configure page for the service you want to edit (found on the Services tab). Under the section labeled "Customize your quick links and add new ones to your custom dashboards," click "Select a dashboard" and choose from your existing dashboards. You can mark one as default. This saved configuration is reused wherever that service appears in Grafana—so every team member gets the same tailored experience without manual adjustments.

How can I create an AI-generated dashboard and set it as default?

Grafana Cloud offers an AI assistant that can generate dashboards based on your cloud provider metrics and services. To create one, use the dashboard creation flow with the AI feature, selecting the appropriate variables and methodology so the dashboard works with your data sources. Once created, add the AI-generated dashboard on the Configure page like any other custom dashboard: go to the service's Configure page, find the quick links section, and add it under "Customize your quick links." If you want users to land on this AI-generated view instead of the out-of-the-box default, simply mark it as the default. The dashboard will then serve as the primary view for that service in the services tab, entity graph, and other entry points. This is a great way to rapidly build a tailored dashboard without manually designing each panel—just describe what you need and let the AI craft it.

How do I customize the instance drill-down view for a cloud service?

Every cloud service in Cloud Provider Observability has an instance-level view—the detailed panels you see when you click on a specific resource, such as a single EC2 instance or a Azure SQL database. You can now customize exactly which panels and queries appear there. Go to the service's Configure page and look for the section "Customize the panels…" (or similar title). Here you can add, remove, or modify panels and their queries. Any changes you make will be reflected everywhere the instance drill-down view is used: in Cloud Provider Observability, Database Observability, the entity graph, and elsewhere. This consistency ensures your team always sees the same relevant metrics when drilling into any instance, regardless of how they entered that view. For example, you could replace a default CPU chart with your own custom query that adds memory or disk I/O, giving a more comprehensive picture at a glance.

Where do I configure these customizations?

All customization for a given cloud service—like Amazon RDS, GCP Cloud SQL, or Azure Virtual Machines—is done from that service's Configure page. To access it, go to the Services tab in Cloud Provider Observability, find the service you want to edit, and click the Configure button. On this page you'll see three main areas: a Preconfigured dashboard (the built-in out-of-the-box view), a list of Custom dashboards you've added (with one marked as default), and Explore-style links for metrics and Grafana Metrics Drilldown. Everything you change here is saved per service and automatically takes effect wherever that service is shown across Grafana—including the services tab, entity graph, Database Observability, and any other entry points. This makes the Configure page a single control center for tailoring cloud observability to your team's needs.

What are the benefits of using custom dashboards in cloud observability?

Customizing dashboards and drill-down views brings several key benefits. First, consistency: once you set a custom default dashboard or tweak the instance panels, those changes appear everywhere in Grafana—no more switching between different views for the same service. Second, efficiency: your team can quickly access the most relevant metrics and panels without navigating away from their workflow, whether they're in Cloud Provider Observability, Database Observability, or the entity graph. Third, flexibility: you can blend out-of-the-box views where they work well, add your own tried-and-tested dashboards, or use AI to rapidly prototype new ones. This means you can tailor observability to match team-specific requirements, such as compliance metrics, SLA tracking, or operational runbooks. Ultimately, customization ensures that the data presented is actionable and aligned with how your organization monitors and troubleshoots cloud resources.