I started with a high-level overview of what a rate limiter is and outlined its importance in various applications. I proposed a token bucket algorithm as a solution, emphasizing its balance between strict limits and burst capacity. The interviewer seemed intrigued but pushed for more details on edge cases, which I tackled but struggled to fully articulate under pressure.

I detailed how I would manage state and store tokens, mentioning in-memory databases like Redis for distributed systems. The interviewer appreciated my idea but asked about scalability concerns. I suggested partitioning strategies but faltered on specifics, which was a noticeable gap in my response.

Things started to pivot when I was asked about how to handle a sudden traffic spike effectively. My initial thinking tripped me up; I focused too much on the theoretical aspects rather than practical implementations. I eventually floated a few ideas about predictive scaling, but it felt a bit cobbled together.