As a DTC store owner, you probably have asked yourself this: should you buy an AI recommendation engine or build one?
The honest answer depends on where your business stands today. If your catalog has general products like apparel and you’re under a million monthly visitors, buying can be wise. Your engineering team’s time won’t be taken up.
Building only makes sense once you’ve outgrown that math. Maybe you sit on data no vendor can use, like subscription patterns or B2B account history. Or maybe you just don’t want your customer data sitting on a platform your competitors use too.
So, if you’re in a dilemma about custom recommendation engine development, this guide walks through both paths to help you choose better.
When Should You Buy an AI Product Recommendation Engine?
Before you plan to choose an AI-focused custom software development provider to buy a recommendation engine, you need to keep plenty of factors in mind.
1. Your Catalog and Data are Generic
Most DTC catalogs run on the same kind of signals: what people click, what they buy together, and basic product attributes. Recommendation vendors have already trained their systems on millions of these patterns across categories just like yours. So they handle new products and new shoppers better than anything you’d build in-house.
Building only makes sense when your data doesn’t fit that standard pattern, like subscription renewal cycles or B2B account structures. It can be a product catalog so specific that generic matching can’t capture how the items actually relate to each other.
2. No Scaling Yet
Under about 1 million monthly visitors, you’re not generating enough interaction data for a custom model to learn from. Recommendation models need volume, thousands of purchases and clicks across your catalog to find patterns that actually hold up. Below that threshold, your data is too thin for a custom system to outperform an off-the-shelf engine, even a well-built one.
So, the vendor’s model, trained across many stores at once, will simply have seen more of the patterns your traffic hasn’t generated yet.
3. Your Team Doesn’t Have Time to Build It
If you have fewer than 20 engineers, adding anyone to a recommendation project can take them off an important project. It’s because building an AI engine is time-consuming.
Timeline makes the case worse. A vendor integration goes live in 6 to 12 weeks, while a custom build takes 6 to 12 months just to ship a single recommendation. By the time your build is running, a vendor solution would have already been improving your conversion for months.
4. You Don’t Have the Budget
Most DTC brands put their money into growth. This includes ads, retention, and new products. Spending a huge amount on custom infrastructure doesn’t always fit that growth plan.
Buying skips this problem entirely. You pay a subscription or usage-based fee that grows with your order volume. The cost scales alongside your revenue instead of a big expense upfront.
When Should You Build an AI Product Recommendation Engine?
Here’s when building actually makes sense, instead of paying for a ready-made engine.
1. A Need to Own the Training Data
When you buy a recommendation engine, the vendor usually keeps the customer behavior data that trains it, with clicks, purchases, and browsing history. That data doesn’t come back to you. This matters when you must follow GDPR rules or when you’re trying to build your first-party data strategy.
So, look at your current vendor’s terms. If they hold onto your behavioral data and don’t let you export it, treat that as a reason to consider building instead.
2. You Have Multiple Brands or Storefronts
Running several sub-brands or regional storefronts means each one has its own catalog and customers. A custom AI engine can pool signals across all of them. So, a shopper’s behavior on one storefront improves recommendations on another, even one they’ve never visited.
For this, you can map out how much catalog and customer overlap exists across your storefronts. If it’s significant, a shared custom system will outperform separate vendor licenses.
3. You Already Run Other ML Systems In-House
If you have infrastructure for fraud detection, demand forecasting, or pricing models, adding a recommendation engine means extending all that. This means your data pipelines, feature stores, and ML team already exist.
Therefore, check what your existing ML team already works with. If your data pipeline already processes customer behavior for another model, building recommendations on top of it will likely cost less.
4. Personalization is Essential for You
If you want to offer a personalized experience to shoppers, recommendations need to react instantly. Think about a flash sale where prices and inventory shift by the minute. Most vendor tools update with a delay. That’s fine for everyday browsing.
So, building a custom engine is better here.
A Few Things to Keep in Mind, Whichever Path You Choose
Buying or building an AI engine isn’t something you’ll do every year. So, you need to put a lot of thought into it and take some steps after you decide on a path. Check out these tips for a head start.
- Read the data export terms before signing anything. Know whether your behavioral data leaves with you if you switch vendors or build later.
- Start with your highest-traffic placement first. Get the product page working well before expanding to cart, email, and search.
- Revisit the decision every 12 months. See how much your business has improved since you made the change.
- Loop in your CX or support team before launch. They’ll be the ones handling complaints if recommendations feel off or repetitive. Their early feedback catches issues faster.
Still not sure whether to buy or build? You can consult an experienced team like CodeClouds, who can offer an AI recommendation engine tailored to your customers and business targets. Get in touch with their team to find out which path will suit you the best.
