Design a Recommendation for Vercel
Last updated: November 17, 2025
Quick Overview
Design a scalable recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Vercel
November 17, 202541
3
427 solved
Design a scalable recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Vercel asks this during the System Design Round to assess your understanding of the full ML lifecycle. They want to see how you translate a business problem into an ML objective, design the feature pipeline, and plan for model monitoring and retraining.
What the Interviewer Expects
- Map the business problem to a concrete ML objective
- Propose reasonable features and a baseline model
- Discuss basic model evaluation metrics
- Outline a simple serving architecture
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How would you handle a 10x increase in prediction requests?
- How would you run A/B tests on different model versions?
- What would you do if model performance degrades over time?
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Requirements
Functional Requirements
- User Interaction: The system should provide personalized recommendations for users based on their previous usage data, preferences, and interactions with other users...
Capacity Estimation
Assuming Vercel has 10 million active users, with each user making an average of 10 requests per day, we can estimate the total requests:
- Total Daily Requests: 10 million users * 10 requests/us...
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