Quickstart: Finding and applying recommendations in context
Learn how to find and apply recommendations in Google Cloud service pages.
Before you begin
For this quickstart, you need a Google Cloud project with a virtual machine running on Compute Engine.
Go to the Google Cloud console.
Select your Google Cloud project. If you don't have a Google Cloud project, create a new project.
Ensure you have sufficient Identity and Access Management (IAM) permissions to view the project's recommendations. If you don have sufficient permission, you might not be able to view some recommendations in detail. Refer to the IAM guide for more information.
Finding VM resource recommendations in context
In this guide, we will use Compute Engine to find recommendations to reduce VM resource costs. Use the next section to find a full list of products with recommendations available in context.
Navigate to the VM Instances dashboard in Compute Engine.
Find the recommendation column under the Recommendation header in the table.
This process might look different for some recommendations, which require you to visit product pages and make the changes there. However, the process is largely the same and can be applied to most recommendations.
Click the recommendation under the Recommendation header in the table to open the detail panel
Click on the name of a recommendation in the Recommendation column to view the detail panel.
Recommendations that you can find in context
Not every recommendation can be found in context. The following Google Cloud services have recommendations in context:
- Cloud SQL
- Cloud Billing
- Cloud Run
- Virtual Machine Instances
- Virtual Machine type
- Virtual Machine Groups
- Instance Groups
- Google Maps Platform
Other ways to apply recommendations
You can also use the Recommendation Hub to find a centralized collection of recommendations available to you.
You can also batch process recommendations using the Google Cloud CLI or REST API. To learn more, refer to the following guide:
- Finding and applying recommendations in the Recommendation Hub
- Enabling the API
- Using the API
- Export recommendations to BigQuery