Design a Feature Flag Service
Last updated: December 12, 2025
Quick Overview
Design a real-time feature flag system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Tesla
December 12, 202548
11
1,104 solved
Design a real-time feature flag system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Tesla asks this during the Technical Screen 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
- Design the full ML lifecycle from data collection to model monitoring
- Address cold start, exploration/exploitation, and model freshness
- Discuss multi-objective optimization and ranking systems
- Plan for model debugging, fairness, and bias mitigation
- Design the feature store and training pipeline for scale
- Address model versioning, canary deployments, and rollback strategies
- Discuss the data flywheel and long-term system evolution
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 run A/B tests on different model versions?
- What is your model retraining strategy?
- How would you debug a model that works well offline but poorly online?
- How would you handle a 10x increase in prediction requests?
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Requirements
- Functional Requirements:
- Ability to create, update, and delete feature flags in real-time.
- Support for targeting different user segments (e.g., by region, user behavior, or device).
- ...
Capacity Estimation
Assuming Tesla has approximately 1 million active users at peak times and each user makes an average of 10 requests per minute to check feature flags:
- Total Requests per Minute: 1,000,000 users ...
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