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To add AI product recommendations to an online store, first decide what the shopper needs, then check whether your ecommerce platform can provide it natively. Use a managed recommendation service only if its data requirements, controls and integration effort fit your store. “AI recommendations” can mean a platform feature, a managed service or custom software; the label alone does not guarantee better results, and not every recommendation system uses generative AI.
Choose the recommendation job and where it belongs
Start with the decision you want the recommendation to help a shopper make. A product page or cart may suit related products or complementary add-ons; a home page may suit personalized picks. These are useful starting points, not universal performance rules.
- Help find a substitute: show related products when a shopper may want another option in the same category.
- Suggest an add-on: show complementary products that can be used with the item being viewed or purchased.
- Surface popular items: show products popular with shoppers, which can be a practical non-personalized option.
- Personalize discovery: use a shopper’s activity to select products for them, provided you have enough permitted interaction data.
Match the recommendation intent to the page and moment. For example, a complementary item can make sense beside a product or in the cart, while personalized discovery may be more useful on a home page. Test the placement rather than assuming one location will perform best.
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Check your platform’s built-in options first
A native feature is often the simplest first implementation: it can reduce integration work and may already use your catalog structure. Shopify, for example, documents product-based recommendations with related and complementary intents through its Ajax Product Recommendations API. The API accepts a product ID, allows a result limit from 1 to 10, and defaults to 10. That is a Shopify API limit, not a claim about other platforms.
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Shopify’s Storefront API returns up to ten products. Shopify describes related recommendations as generated from sales data, product descriptions and collection relationships. Complementary recommendations require manual configuration in the Search & Discovery app; Shopify also says that app can affect recommendations and search results.
Check your own platform’s documentation for available recommendation intents, configuration, API limits and catalog requirements. If the native feature meets the need, an external service may add complexity without a clear benefit.
Check whether you have enough interaction data
Personalized recommendations depend on usable interaction history. Amazon Web Services (AWS) publishes specific minimums for Amazon Personalize ecommerce use cases: at least 1,000 item-interaction records and 25 unique user IDs, each with at least two item interactions. AWS recommends 50,000 item interactions from at least 1,000 users with two or more interactions each for quality recommendations. That larger figure is AWS guidance, not a guaranteed result or an industry-wide threshold.
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These requirements are specific to Amazon Personalize, not a universal rule for every system. They do illustrate why a new or low-traffic store should not assume that a managed service can immediately deliver robust personalization. For AWS’s use cases and qualifications, see its ecommerce use-case documentation.
If your store has little interaction history, consider using a platform-native related or complementary feature, or popular items that do not depend on individual shopper histories, while collecting permitted events. This is a practical inference from AWS’s published data requirements, not a vendor guarantee.
Choose between a native feature, managed service and custom software
Once you know the shopper goal and what data you can use, compare implementation routes against specific requirements rather than marketing claims. The right choice depends on your commerce platform, event history, engineering capacity, privacy requirements and the controls you need.
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| Route | What it can suit | What to verify |
|---|---|---|
| Native platform feature | A first implementation using supported product or shopper context, with less custom integration. | Available intents, configuration, result limits, catalog behavior and whether the feature meets your placement needs. |
| Managed recommendation service | Stores that need documented use cases or controls beyond the native option and can meet the service’s data and integration requirements. | Interaction-data thresholds, supported use cases, update behavior, filters, regional availability, technical requirements, pricing and data terms. |
| Custom software | Teams with a specific recommendation logic or integration need that existing options do not meet. | Who will build and maintain it, what data and permissions it requires, and how it will be evaluated. The vendor documentation cited here does not establish a neutral comparison of custom implementations. |
For example, AWS documents ecommerce use cases including popular items, frequently bought together, viewed-X-also-viewed and recommended-for-you. It also documents updating recommendations from newly recorded activity for real-time personalization, along with promotions for some use cases and custom filters. See the Amazon Personalize recommendations documentation for the current details.
Google describes Agent Search recommendations, formerly called Recommendations from Vertex AI Search, as a managed real-time personalization capability for retail. Its documentation describes business rules and filters, including availability and custom tags. Check the Google Cloud recommendations page for current service naming and capabilities. Confirm each provider’s regional availability, technical requirements and pricing directly; the vendor descriptions do not establish which option performs best in a head-to-head comparison.
Integrate recommendations without losing catalog control
Recommendation results are useful only if they map to products shoppers can actually view and buy. Before launch, confirm how the integration resolves product IDs to current catalog records and how it handles unavailable or already purchased products when those should be excluded.
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- Check whether out-of-stock products can be filtered out or suppressed.
- Decide whether products already bought by a shopper should be excluded for the relevant placement.
- Confirm that returned product identifiers map to current product pages, variants and images in your storefront.
- Check whether recommendations update when product availability or catalog details change.
AWS documents filtering examples, and Google documents availability filtering, but controls differ by service and implementation. Verify the exact behavior you will deploy rather than assuming that a filter works the same way across vendors.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Review data access, privacy terms and permissions
Before connecting a third-party AI tool or recommendation service, identify what shopper or store data it receives, what access it requests, whether it needs permission to change your store, and how long it retains data. Shopify warns that data connected to third-party AI tools is processed under the provider’s terms and privacy policy. It advises merchants to review those terms, their obligations to customers and any changes the tool may make. Read its AI tool considerations before granting access.
- List the data fields and events sent to the provider.
- Check the provider’s data-retention and privacy terms.
- Request only the access the integration needs; determine whether write access is necessary.
- Establish how proposed changes are reviewed before they affect the storefront or catalog.
Privacy and customer-notice obligations depend on your circumstances and applicable law, so do not treat a vendor integration guide as legal advice.
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Measure whether the recommendations help
Instrument the experience before launch so you can evaluate it against what shoppers see today. Track recommendation impressions and clicks, along with add-to-cart and purchase outcomes. Where feasible, compare the recommendation experience with the existing one using a controlled test.
Interpret results carefully: a click or purchase after a recommendation does not, by itself, show that the recommendation caused it. Account for inventory, discounts, seasonality and placement changes when comparing outcomes. These are measurement practices, not performance statistics established by the vendor documentation cited here; it provides no neutral benchmark proving that AI recommendations increase sales.
Keep a human review loop after launch
Check recommendations regularly for relevance, current availability and unintended outcomes. Shopify’s guidance is direct: “AI outputs can contain errors, so review any information or action that you ask the AI tool to take to confirm its accuracy.” A recommendation system should not be left unchecked simply because its output is automated.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

