Recommendations AI (Beta)

Name: Recommendations AI (Beta) API
Brand: Recommendations AI (Beta)
Availability: InStock

✓ Official Vendor SpecAI/MLMl Inferenceoauth217 EndpointsREST

For Agents

Manage product catalogs, ingest user events, and call serving endpoints to retrieve personalized recommendations from Google's Recommendations AI Beta service.

Quickstart

Get started with Recommendations AI (Beta) in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"predict product recommendations for a user"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with Recommendations AI (Beta) API.

Create and manage catalogs that hold the items eligible for recommendation

Import catalog items from Cloud Storage or BigQuery in bulk

Ingest user events (detail-page-view, add-to-cart, purchase) that train recommendation models

GET STARTED

Start building with Recommendations AI (Beta) API

Explore with Jentic

View OpenAPI Document

Use for: I need to retrieve product recommendations for a user viewing a product detail page, Import a fresh catalog of products from BigQuery into Recommendations AI, Ingest a batch of user events from yesterday into the recommendation model, Register a new prediction API key for our front-end app

Not supported: Does not handle full-text search, fraud scoring, or marketing campaign delivery — use for catalog management, user event ingestion, and recommendation serving only.

Recommendations AI (Beta) is Google Cloud's managed personalization service for predicting catalog items a user is likely to engage with next. The API exposes operations to manage catalogs and catalog items, ingest user events, register prediction API keys, and call serving endpoints that return ranked recommendations. Google strongly recommends new customers use the Retail API, which incorporates the GA version of these capabilities; this beta API remains for existing integrations.

Use Cases

Patterns agents use Recommendations AI (Beta) API for, with concrete tasks.

★ Product Detail Page Recommendations

On a product detail page, the storefront calls predict with the current user, page event context, and the placement (e.g., others-you-may-like) to receive a ranked list of recommended catalog items. Predictions are personalized by the user-event history Recommendations AI has been trained on.

Call predict on placement projects/p/locations/global/catalogs/default_catalog/eventStores/default_event_store/placements/others_you_may_like with the user event payload for the current visitor.

Catalog and Event Backfill

Before recommendations are useful, the catalog must be imported and historical user events ingested. Recommendations AI provides import operations on catalogItems and userEvents that can pull from Cloud Storage or BigQuery, making it possible to backfill months of history in a single pipeline run.

Call POST /v1beta1/{+parent}/catalogItems:import with a Cloud Storage URI containing the catalog feed, then POST /v1beta1/{+parent}/userEvents:import for the historical event log.

Restricted Prediction Keys for Client Apps

Front-end apps should call predict with restricted, per-app API keys rather than full Cloud credentials. The API exposes predictionApiKeyRegistrations so each client app gets its own key tied to a single event store and catalog, reducing blast radius if a key leaks.

Register a prediction API key for our mobile app under the default event store, then ship the key to the app via remote config.

Agent-Driven Recommendation Operations

A merchandising agent uses Jentic to refresh catalog imports, ingest events, and call predict on demand to populate suggestion modules in marketing emails or off-site placements, without needing direct console access to the Recommendations AI project.

Search Jentic for predict product recommendations, load the schema for the placements predict operation, and execute it with the user and event context for a marketing email recipient.

Key Endpoints

17 endpoints — recommendations ai (beta) is google cloud's managed personalization service for predicting catalog items a user is likely to engage with next.

METHOD

PATH

DESCRIPTION

POST

/v1beta1/{+name}:predict

Retrieve ranked recommendations for a user event

POST

/v1beta1/{+parent}/catalogItems:import

Bulk import catalog items from GCS or BigQuery

POST

/v1beta1/{+parent}/userEvents:import

Bulk import historical user events

POST

/v1beta1/{+parent}/userEvents:collect

Collect a single user event in real time

GET

/v1beta1/{+parent}/catalogItems

List catalog items

GET

/v1beta1/{+parent}/predictionApiKeyRegistrations

List registered prediction API keys

POST

/v1beta1/{+name}:predict

Retrieve ranked recommendations for a user event

POST

/v1beta1/{+parent}/catalogItems:import

Bulk import catalog items from GCS or BigQuery

POST

/v1beta1/{+parent}/userEvents:import

Bulk import historical user events

POST

/v1beta1/{+parent}/userEvents:collect

Collect a single user event in real time

GET

/v1beta1/{+parent}/catalogItems

List catalog items

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Google OAuth 2.0 credentials and prediction API keys are stored encrypted in the Jentic vault. Scoped short-lived tokens are issued for control-plane calls and prediction keys are routed through the vault for predict and collect, so neither raw credential ever enters the agent context.

Intent-based discovery

Agents search Jentic by intent (predict recommendations, import a catalog, ingest user events) and Jentic returns the matching Recommendations AI operation with its input schema, removing the need to read the v1beta1 discovery doc.

Time to first call

Direct integration takes 1-2 weeks because the catalog schema, event taxonomy, and placement setup all need to be modelled. Through Jentic: under 1 hour for individual operations once the catalog and event store are configured.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Discovery Engine API

GA-era Google Cloud service that incorporates personalization and search capabilities

Choose Discovery Engine for new builds that need search and personalization together; choose Recommendations AI Beta only when extending an existing v1beta1 integration.

Complementary

BigQuery API

Source for catalog and user event imports

Use BigQuery as the source dataset for catalogItems:import and userEvents:import bulk loads.

Complementary

Cloud Storage API

Holds JSON or CSV feeds used for bulk imports

Use Cloud Storage to stage catalog feeds and event logs ahead of import jobs into Recommendations AI.

FAQs

Specific to using Recommendations AI (Beta) API through Jentic.

What authentication does the Recommendations AI Beta API use?

The API uses Google OAuth 2.0 with the cloud-platform scope for control-plane and import calls, and registered prediction API keys for client-side predict and collect calls. Through Jentic the OAuth credentials live in the vault and short-lived tokens are minted per call.

Should I use this API for new projects?

Google recommends new customers use the Retail API, which incorporates the GA version of these capabilities; this v1beta1 surface is maintained primarily for existing integrations. The migration path is documented in the Retail API docs.

What are the rate limits for Recommendations AI?

Predict QPS, import job, and event ingestion quotas are documented per project in the Recommendations AI quotas page. Import jobs are long-running operations and finish asynchronously, while predict calls are synchronous and sized for serving traffic.

How do I serve recommendations through Jentic?

Search Jentic for predict product recommendations, load the schema for the placements predict operation which maps to POST /v1beta1/{+name}:predict, and execute it with the userEvent payload and the placement resource name.

Is the Recommendations AI API free?

No, prediction calls and training data are billed according to the Recommendations AI pricing page. Catalog and event imports also incur Cloud Storage or BigQuery read charges depending on the source.

How do I ingest user events efficiently?

Use POST /v1beta1/{+parent}/userEvents:import for historical or bulk loads from Cloud Storage or BigQuery, and POST /v1beta1/{+parent}/userEvents:collect for low-latency single-event ingestion from browsers and apps.