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This page describes how to optimize the performance of your AlloyDB for PostgreSQL clusters by using the
underprovisioned cluster recommender.
The recommender helps you detect clusters that have high CPU and memory
utilization and provides recommendations for improving your cluster configuration.
How the underprovisioned cluster recommender works
When there is high CPU and or memory utilization detected, you see a
recommendation to increase the size of the affected instance in the cluster
to reduce CPU or memory utilization at peak. Recommendations are generated daily.
Before you begin
Before you can view recommendations and insights, do the following:
GET https://recommender.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/recommenders/google.alloydb.cluster.PerformanceRecommender/recommendations?filter=recommenderSubtype=INCREASE_PRIMARY_INSTANCE_SIZE
Replace the following:
PROJECT_ID: Your project ID.
LOCATION: A region where your clusters are located, such as us-central1.
View insights and detailed recommendations
You can view insights and detailed recommendations about underprovisioned clusters
that require optimization using the Google Cloud console,
gcloud CLI, or the Recommender API.
Console
In the Google Cloud console, go to the Clusters page.
GET https://recommender.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/insightTypes/google.alloydb.cluster.PerformanceInsight/insights?filter=insightSubtype=INSIGHT_SUBTYPE
Replace the following:
PROJECT_ID: Your project ID.
LOCATION: A region where your clusters are located, for example, us-central1.
INSIGHT_SUBTYPE: set this parameter to one of the following:
HIGH_INSTANCE_CPU_UTILIZATION: display
insights about CPU usage
HIGH_INSTANCE_MEMORY_UTILIZATION: display
insights about memory
The following table lists the insights and recommendations that the AlloyDB for PostgreSQL
underprovisioned cluster recommender might generate to help you avoid bottlenecks from high CPU and memory
usage and minimize the likelihood of out-of-memory events.
The subtypes are visible in the gcloud and API results.
Insights
Recommendations
Based on the current CPU utilization trends, the cluster is flagged as
having high CPU usage.
Subtype: HIGH_INSTANCE_CPU_UTILIZATION
Increase CPU size or reduce CPU utilization.
Subtype: INCREASE_PRIMARY_INSTANCE_SIZE
Based on the current memory utilization trends, the cluster is flagged
as having high memory usage.
Subtype: HIGH_INSTANCE_MEMORY_UTILIZATION
Increase memory size or reduce memory utilization.
Subtype: INCREASE_PRIMARY_INSTANCE_SIZE
Apply recommendations using the Google Cloud console
Evaluate the recommendations carefully and do the following in the
Google Cloud console to implement the recommendation:
Click Edit on your cluster.
In the Edit primary instance window, switch to a machine type with more vCPUs and more memory.
You don't need to rightsize the cluster exactly as recommended. Use your
judgement and resize based on how you intend to provision the cluster.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-26 UTC."],[[["\u003cp\u003eThe underprovisioned cluster recommender identifies clusters with high CPU and/or memory utilization and suggests optimizations to enhance performance.\u003c/p\u003e\n"],["\u003cp\u003eRecommendations to increase the instance size of underprovisioned clusters are generated daily and can be viewed after enabling the Recommender API and having the correct IAM roles.\u003c/p\u003e\n"],["\u003cp\u003eYou can list and apply underprovisioned cluster recommendations using the Google Cloud console, gcloud CLI, or the Recommender API.\u003c/p\u003e\n"],["\u003cp\u003eInsights on high CPU and memory utilization can be viewed via the console, CLI, or API, detailing the type of usage issue, such as \u003ccode\u003eHIGH_INSTANCE_CPU_UTILIZATION\u003c/code\u003e or \u003ccode\u003eHIGH_INSTANCE_MEMORY_UTILIZATION\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003eImplementing the recommended instance size increase involves editing the cluster settings in the console, updating the primary instance to a machine type with more vCPUs and memory.\u003c/p\u003e\n"]]],[],null,["# Optimize underprovisioned clusters\n\nThis page describes how to optimize the performance of your AlloyDB for PostgreSQL clusters by using the\nunderprovisioned cluster [recommender](/recommender/docs/overview).\nThe recommender helps you detect clusters that have high CPU and memory\nutilization and provides recommendations for improving your cluster configuration.\n\nHow the underprovisioned cluster recommender works\n--------------------------------------------------\n\nWhen there is high CPU and or memory utilization detected, you see a\nrecommendation to increase the size of the affected instance in the cluster\nto reduce CPU or memory utilization at peak. Recommendations are generated daily.\n\nBefore you begin\n----------------\n\nBefore you can view recommendations and insights, do the following:\n\n- Ensure that you [enable the Recommender API](/recommender/docs/enabling).\n\n- To get the permissions to view and work with insights and recommendations,\n ensure that you have the required [Identity and Access Management (IAM) roles](/iam/docs/understanding-roles#cloud-alloydb-roles).\n\n \u003cbr /\u003e\n\n See [Grant access to other users](/alloydb/docs/user-grant-access) for more information.\n\nList underprovisioned cluster recommendations\n---------------------------------------------\n\nYou can list recommendations for underprovisioned clusters\nusing the Google Cloud console, `gcloud CLI`, or the Recommender API. \n\n### Console\n\nTo list recommendations about underprovisioned clusters, complete the following steps:\n\n1. In the Google Cloud console, go to the **Clusters** page.\n\n [Go to Clusters](https://console.cloud.google.com/alloydb/clusters)\n\n For more information, see\n [Find recommendations with Recommendation Hub](/recommender/docs/recommendation-hub/identify-configuration-problems).\n2. In the **Performance** card, click **Underprovisioned primary instance**.\n\n A list of clusters to which the **Underprovisioned primary instance** recommendation applies is displayed.\n\n### gcloud CLI\n\nTo list recommendations about underprovisioned clusters using gcloud CLI, run the [`gcloud recommender recommendations list`](/sdk/gcloud/reference/recommender/recommendations/list) command as follows: \n\n```\ngcloud recommender recommendations list \\\n--project=PROJECT_ID \\\n--location=LOCATION \\\n--recommender=google.alloydb.cluster.PerformanceRecommender \\\n--filter=recommenderSubtype=INCREASE_PRIMARY_INSTANCE_SIZE\n```\n\nReplace the following:\n\n- \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: Your project ID.\n- \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e: A region where your clusters are located, such as `us-central1`.\n\n### API\n\nTo list recommendations for underprovisioned clusters using the [Recommendations API](/recommender/docs/using-api), call the\n[`recommendations.list`](/recommender/docs/reference/rest/v1/projects.locations.recommenders.recommendations/list)\nmethod as follows: \n\n```\nGET https://recommender.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/recommenders/google.alloydb.cluster.PerformanceRecommender/recommendations?filter=recommenderSubtype=INCREASE_PRIMARY_INSTANCE_SIZE\n```\n\nReplace the following:\n\n- \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: Your project ID.\n- \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e: A region where your clusters are located, such as `us-central1`.\n\nView insights and detailed recommendations\n------------------------------------------\n\nYou can view insights and detailed recommendations about underprovisioned clusters\nthat require optimization using the Google Cloud console,\n`gcloud CLI`, or the Recommender API. \n\n### Console\n\n1. In the Google Cloud console, go to the **Clusters** page.\n\n [Go to Clusters](https://console.cloud.google.com/alloydb/clusters)\n2. Click the recommendation button for a cluster in the **Issues** column.\n\n The recommendation panel appears, which contains insights and detailed recommendations about an underprovisioned cluster.\n\n### gcloud CLI\n\nRun the [`gcloud recommender insights list`](/sdk/gcloud/reference/recommender/insights/list) command as follows: \n\n```\ngcloud recommender insights list \\\n--project=PROJECT_ID \\\n--location=LOCATION \\\n--insight-type=google.alloydb.cluster.PerformanceInsight\n--filter=insightSubtype=INSIGHT_SUBTYPE\n```\n\nReplace the following:\n\n- \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: Your project ID.\n- \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e : A region where your clusters are located, such as `us-central1`.\n- \u003cvar translate=\"no\"\u003eINSIGHT_SUBTYPE\u003c/var\u003e: set this parameter to one of the following:\n - `HIGH_INSTANCE_CPU_UTILIZATION`: display insights about CPU usage\n - `HIGH_INSTANCE_MEMORY_UTILIZATION`: display insights about memory\n\n### API\n\nCall the [`insights.list`](/recommender/docs/reference/rest/v1/projects.locations.insightTypes.insights/list) method as follows: \n\n```\nGET https://recommender.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/insightTypes/google.alloydb.cluster.PerformanceInsight/insights?filter=insightSubtype=INSIGHT_SUBTYPE\n```\n\nReplace the following:\n\n- \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: Your project ID.\n- \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e: A region where your clusters are located, for example, `us-central1`.\n- \u003cvar translate=\"no\"\u003eINSIGHT_SUBTYPE\u003c/var\u003e: set this parameter to one of the following:\n - `HIGH_INSTANCE_CPU_UTILIZATION`: display insights about CPU usage\n - `HIGH_INSTANCE_MEMORY_UTILIZATION`: display insights about memory\n\nThe following table lists the insights and recommendations that the AlloyDB for PostgreSQL\nunderprovisioned cluster recommender might generate to help you avoid bottlenecks from high CPU and memory\nusage and minimize the likelihood of out-of-memory events.\nThe subtypes are visible in the `gcloud` and API results.\n\nApply recommendations using the Google Cloud console\n----------------------------------------------------\n\nEvaluate the recommendations carefully and do the following in the\nGoogle Cloud console to implement the recommendation:\n\n1. Click **Edit** on your cluster.\n2. In the **Edit primary instance** window, switch to a machine type with more vCPUs and more memory.\n You don't need to rightsize the cluster exactly as recommended. Use your\n judgement and resize based on how you intend to provision the cluster.\n\n3. Click **Update instance**.\n\n | **Note:** You must carefully evaluate before you update the cluster. Applying recommendations might impact your pricing.\n\nWhat's next\n-----------\n\n- [Google Cloud recommenders](/recommender/docs/recommenders)"]]
