Shaped is quickly becoming a leading tool for adding personalization into any product or website. Developers benefit because of how quickly it is to connect, train and deploy state-of-the-art recommendation models for their discovery use-cases.

Shaped isn’t just any recommendation API, though — it provides semantic understanding of your unstructured user and item data and can understand your user’s session in real-time.

AWS, a cloud computing platform released AWS Personalize for a subset of personalization use-cases in 2019. This posts dives into the trade-offs of using Shaped compared to AWS Personalize.

The top four differences

1. Shaped connects to and transforms your raw data directly

Companies typically store data across multiple data stores and applications. For example, users and items may be stored in BigQuery and events may be logged to Amplitude.

Shaped connects directly to all these data sources, and provides the pipelines to ingest and transform the data in real-time. To get started you just need to use Shaped’s declarative SQL API to specify where your data is, who you want to personalize for and what items you want to be ranked.

Setting up AWS Personalize, is basically like setting up a CMS, you need to manage all the data engineering work to keep your catalogues of items and users in sync. Feeding real-time events is error-prone as you’re required to manually push them. These extra steps require more time, effort and on-going maintenance for your engineers.

2. Real-time session based ranking

If you’ve ever used TikTok, you’ll know how good it is at recommending personalized content that reacts to every like, comment, watch or click you make. For example, if you start interacting with cat videos within a session, TikTok is going to show you more cat videos within that session. Real-time session based ranking is particularly good improving the experience for cold-start users or anonymous traffic. We’ve added the same technology to Shaped, allowing you to get the same reactive real-time recommendations that you see in leading social products. As AWS Personalize on the other hand, requires you to push data to their cloud service, they don’t have the same level of real-time support as Shaped. For more information see our blog post.

3. Companies using Shaped get white-glove treatment

Our team of machine-learning engineers from FAANG will set up your initial models and discuss your business objectives with you. This will save you hours during setup and potentially months of experimentation time. We’ll explain how it works, what features are important and share performance insights with you regularly. While machine-learning is complicated, we’re not a black box. It’s extremely important to us that the results are interpretable and you understand how your models work.

4. Technology designed to solve the cold start problem

We use state-of-the-art machine-learning techniques and pre-train our models so you don’t need much data to get started. In addition, we support unstructured data types without you having to manually tag the metadata of your items and users. This gives our models better signals and helps deal with the cold start problem.

Shaped vs. AWS Personalize

Ease of use and maintenance

Technology

What about pricing?

AWS is known for their confusing pricing and their Personalize product is no exception. You’ll need a spreadsheet to model an hourly estimate and then forecast your monthly cost based on peaks and troughs in usage. With AWS you need to monitor your pricing like a hawk. There are always gotcha moments where you find out you’ve been stung with hundreds or thousands extra on your invoice 🤬

At Shaped we want to keep things simple and focus on the things that matter so we do flat-fee monthly pricing based on forecasted usage and compute requirements. Once we’ve discussed your users/items/events we can provide a number to get started.

Thanks for reading, if you have any questions or want to discuss personalization for your product feel free to reach out to hello@shaped.ai