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Setup includes information about setting up a project for
Vertex AI Feature Store (Legacy) and the required permissions for using
Vertex AI Feature Store (Legacy).
Configure project
The following procedure describes how to create a new project and enable the
Vertex AI API. This API is required to use
Vertex AI Feature Store (Legacy). If you already have an existing project with
the Vertex AI API enabled, you can use that project instead of
creating a new project.
Sign in to your Google Cloud account. If you're new to
Google Cloud,
create an account to evaluate how our products perform in
real-world scenarios. New customers also get $300 in free credits to
run, test, and deploy workloads.
In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
In addition to user permissions, Vertex AI Feature Store (Legacy) acts on your
behalf to perform operations such as accessing source data. To do so,
Vertex AI Feature Store (Legacy) uses a service agent:
service-PROJECT_NUMBER@gcp-sa-aiplatform.iam.gserviceaccount.com.
By default, the service agent grants Vertex AI Feature Store (Legacy) access
to source data in the same project where your featurestore is located. If the
source data is in a different project from your featurestore, you must grant the
service agent permission to access the project where the source data is
located.
Vertex AI admins have Vertex AI Feature Store (Legacy) administrator
privileges. If you require more granularity, Vertex AI Feature Store (Legacy)
provides a set of predefined IAM roles. These roles provide
different sets of permissions that are based on the following personas:
IT operations and DevOps
IT operations and DevOps manage Google Cloud resources and are responsible for
creating featurestores and tuning their performance. You can use the
featurestoreAdmin or featurestoreInstanceCreator role. The instance creator
role lets you manage featurestores but prevents you from viewing data or
writing data to the featurestores.
Data scientists and data engineers
Data scientists and data engineers create features and write data to
featurestores. You can use the featurestoreResourceEditor role to
manage entity types and features, and use the featurestoreDataWriter role to
read and write feature values.
ML researchers and business analysts
ML researchers and business analysts search for features and export values for
training models or making predictions; they don't need to create new features or
write data. You can use the featurestoreResourceViewer role to search
or browse for features and the featurestoreDataViewer role to read feature
values.
Vertex AI Feature Store (Legacy) enforces quotas and limits to help you manage
resources by setting your own usage limits and to protect the community of
Google Cloud users by preventing unforeseen spikes in usage. To prevent you from
hitting unplanned constraints, review Vertex AI Feature Store (Legacy) quotas
on the Quotas and limits page. For example,
Vertex AI Feature Store (Legacy) sets a quota on the number of online serving
nodes and a quota on the number of online serving requests that you can make per
minute.
[[["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 2025-08-28 UTC."],[],[],null,["# Setup includes information about setting up a project for\nVertex AI Feature Store (Legacy) and the required permissions for using\nVertex AI Feature Store (Legacy).\n\nConfigure project\n-----------------\n\nThe following procedure describes how to create a new project and enable the\nVertex AI API. This API is required to use\nVertex AI Feature Store (Legacy). If you already have an existing project with\nthe Vertex AI API enabled, you can use that project instead of\ncreating a new project.\n\n- Sign in to your Google Cloud account. If you're new to Google Cloud, [create an account](https://console.cloud.google.com/freetrial) to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Vertex AI API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com)\n\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Vertex AI API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com)\n\nVertex AI Feature Store (Legacy) service agent\n----------------------------------------------\n\nIn addition to user permissions, Vertex AI Feature Store (Legacy) acts on your\nbehalf to perform operations such as accessing source data. To do so,\nVertex AI Feature Store (Legacy) uses a service agent:\n`service-`\u003cvar class=\"readonly\" translate=\"no\"\u003ePROJECT_NUMBER\u003c/var\u003e`@gcp-sa-aiplatform.iam.gserviceaccount.com`.\nBy default, the service agent grants Vertex AI Feature Store (Legacy) access\nto source data in the same project where your featurestore is located. If the\nsource data is in a different project from your featurestore, you must grant the\nservice agent permission to access the project where the source data is\nlocated.\n\nFor more information, see [Grant Vertex AI service agents access to other\nresources](/vertex-ai/docs/general/access-control#grant_service_agents_access_to_other_resources).\n\nIAM permissions\n---------------\n\nVertex AI admins have Vertex AI Feature Store (Legacy) administrator\nprivileges. If you require more granularity, Vertex AI Feature Store (Legacy)\nprovides a set of predefined IAM roles. These roles provide\ndifferent sets of permissions that are based on the following personas:\n\nIT operations and DevOps\n: IT operations and DevOps manage Google Cloud resources and are responsible for\n creating featurestores and tuning their performance. You can use the\n `featurestoreAdmin` or `featurestoreInstanceCreator` role. The instance creator\n role lets you manage featurestores but prevents you from viewing data or\n writing data to the featurestores.\n\nData scientists and data engineers\n: Data scientists and data engineers create features and write data to\n featurestores. You can use the `featurestoreResourceEditor` role to\n manage entity types and features, and use the `featurestoreDataWriter` role to\n read and write feature values.\n\nML researchers and business analysts\n: ML researchers and business analysts search for features and export values for\n training models or making predictions; they don't need to create new features or\n write data. You can use the `featurestoreResourceViewer` role to search\n or browse for features and the `featurestoreDataViewer` role to read feature\n values.\n\nFor descriptions of each role and their associated permissions, see\n[Predefined roles for\nVertex AI](/vertex-ai/docs/general/access-control#predefined-roles).\n\nQuotas and limits\n-----------------\n\nVertex AI Feature Store (Legacy) enforces quotas and limits to help you manage\nresources by setting your own usage limits and to protect the community of\nGoogle Cloud users by preventing unforeseen spikes in usage. To prevent you from\nhitting unplanned constraints, review Vertex AI Feature Store (Legacy) quotas\non the [Quotas and limits](/vertex-ai/quotas#featurestore) page. For example,\nVertex AI Feature Store (Legacy) sets a quota on the number of online serving\nnodes and a quota on the number of online serving requests that you can make per\nminute.\n\nWhat's next\n-----------\n\n- Learn about [Manage featurestores](/vertex-ai/docs/featurestore/managing-featurestores).\n- Learn about [best practices](/vertex-ai/docs/featurestore/best-practices) for using Vertex AI Feature Store (Legacy)."]]