Recommender uses machine learning to provide detailed and high quality insights. Insights are findings that you can use to proactively focus on important patterns in resource usage. You can use insights independently from recommendations. Some insights link to recommendations and provide evidence for the associated recommendations.
This page describes key concepts for interpreting and using insights.
Each insight has a specific insight type. Insight types are specific to a single Google Cloud product and resource type. A single product can have multiple insight types, where each provides a different type of insight for a different resource.
Each insight type has a unique insight type ID that identifies the service
internally. You use the insight type ID when interacting with insights using the
gcloud commands, or
the REST or
For more information, see Insight types.
An insight is a machine-generated finding that may be linked to one or more recommendations. An insight has the following core attributes:
- Insight Subtype
- Target Resources
- State Info
- Last Refresh Time
- Observation Period
- Recommendation reference
The insight name is stored in the
name field of the Insight entity. It has
the following format:
- TARGET_PROJECT_ID is the ID of the project where the insight was generated.
- LOCATION is the GCP
location where resources associated with the
insight are located (for example,
- INSIGHT_TYPE_ID is the fully-qualified insight type ID (for example,
- INSIGHT_ID is a unique ID for the insight
This is human-readable summary of the insight. It is only available in English.
Each insight type may support multiple subtypes. The content schema is stable for a given subtype.
Structured fields that include insight details. Content schema is determined by
insight type and subtype. For example,
Similar to impacts for recommendations, there are categories for insights:
Fully qualified resource names of the GCP resources the insight is targeting.
Insights go through multiple state transitions after they are proposed:
ACTIVE, which means that the insight has been generated, but no actions have been taken in response. Content for active insights is updated when the underlying data changes. Active insights can be marked
ACCEPTED, which means that some action has been taken based on the insight. Insights become accepted when an associated recommendation has been marked
FAILED. Insights can also be accepted directly. Content for accepted insights is immutable. Accepted insights are retained for 90 days from the time of the state change.
DISMISSED, which means that the insight has been dismissed without taking any action based on it. Content for dismissed insights is updated when the underlying data changes.
When directly marking an insight ACCEPTED, you can include additional metadata
about the operation with state metadata. The metadata is specified as
key:value pairs. Updates to the state metadata field overwrite any existing
An etag is a unique fingerprint that identifies the current state of an
insight. Each time the insight changes, a new etag value is assigned.
In order to change insight state, you must provide the etag of the existing insight. This makes sure that any operations are performed only if the insight has not changed since you last retrieved it.
- An outage (for a performance impact)
- A compromise (for a security impact)
- An overspend (for a cost impact)
- A mismanagement (for a manageability impact)
This field comes with values
LOW set as the
Each insight type can have its own
severity strategy defined.
Last Refresh Time
The last refresh time indicates the freshness of the data used to generate the insight.
Observation period is the time period leading up to the insight. The source data
used to generate the insight ends at
last_refresh_time and begins at
Reference to an associated recommendation. References link insights with their associated recommendations. This field is empty when there are no recommendations derived from the insight.