The Cost of Recommendation Engines

[mouakter13]

Recommendation engines are an excellent way to customize user experience. They analyze user data and activity to suggest products, services, and content your customers might like next. For instance, at the end of this article, you’ll find a “related articles” list — that’s a recommendation engine in action.

These recommenders are a win-win: Customers find what they want, and companies get to boost user retention on their platform.

But what's the actual cost for companies to understand country wise email marketing list my preferences (and yours!) so accurately? The answer depends on the kind of recommendation engine they use.

Platform-integrated: Typically free. Many ecommerce, marketing, or CMS platforms include basic recommendation capabilities free of charge or at a minimal cost. Examples include Shopify’s product recommendation API and Hubspot’s smart content recommendations.
Off-the-shelf: $2000 - $12,000. These are typically SaaS-based solutions, with a pay-as-you-go model. For instance, Amazon Personalize computes its pricing based on data sent to the model, training, and real-time or batch recommendations.
Custom: $10,000 - $200,000. A custom recommendation engine might be the right fit if your business model depends on curating good content or products. These can be expensive, but you can use open-source libraries like LightFM and FAISS to build quick prototypes. Examples include Netflix, Amazon, and Spotify.

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The Cost of Process Automation Solutions
As I mentioned earlier, my AI journey began with process automation tools. I built a dashboard for managing access to internal company tools.

Instead of manually reviewing and approving each user request, my script would verify eligibility, grant permissions, and notify users automatically. It would also flag unusual access requests or suggest likely permissions based on data from similar teammates.

While working on this project, I discovered that process automation can handle any repetitive task. These tools can open new browser tabs, click buttons, send customized emails, log activities, and more. When you add AI to the mix, these systems can even handle decision-making and analysis based on previous data.