I went straight to token bucket and sliding window, which felt solid, but I underestimated how much the freemium angle would matter.
Start by clarifying the freemium model's tiers and rate limiting goals (e.g., prevent abuse, encourage upgrades). Then propose a distributed rate limiter design (e.g., token bucket with Redis) that enforces per-tier limits, and discuss trade-offs like accuracy vs. latency, and how to handle bursts and scaling.
Pro tip: Tie rate limiting directly to business metrics: suggest that limits should be dynamically adjustable per tier and monitored to optimize conversion from free to paid. Also, mention that rate limiting can be a feature differentiator (e.g., higher burst limits for paid users).
Ask about the freemium tiers (e.g., free, pro, enterprise), expected traffic patterns, and whether rate limiting is per user, per API key, or per IP. Also clarify if limits should be hard or soft (e.g., throttling vs. blocking).
Select an algorithm like token bucket or sliding window that balances accuracy and performance. Token bucket is often preferred for its ability to handle bursts and its simplicity in distributed settings.
Use a centralized data store like Redis with atomic operations (e.g., Lua scripts) to enforce limits across multiple instances. Consider sharding or local caching with synchronization to reduce latency and single points of failure.
Define rate limits per tier (e.g., free: 100 req/min, pro: 1000 req/min) and store them in a configuration service. Ensure the rate limiter can dynamically fetch limits based on user subscription.
Address trade-offs: strict limits may frustrate users, while lenient limits risk abuse. Propose monitoring (e.g., Prometheus metrics) and alerting on limit breaches, and suggest A/B testing to optimize limits for conversion.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.