The interviewer asked me to design a rate limiter. I started by discussing different types of rate limiting strategies, including token bucket and leaky bucket algorithms. This seemed to resonate with the interviewer, who nodded as I laid out the concepts and their trade-offs. When I mentioned using Redis for distributed limiting, he interjected with a question about consistency and what would happen if we had nodes out of sync.

I had to think on my feet here. I explained the potential issues, but I wasn’t confident enough in my follow-up regarding handling edge cases in a distributed environment. The interviewer asked how I would handle a high request load efficiently, pushing me to consider algorithms that could help balance throughput and fairness. I suggested exponential backoff but didn't elaborate enough on the implications for user experience, which might have been a missed opportunity.

As I drew out the system, I focused on scalability. I proposed a microservice architecture, detailing how each service could independently scale while sharing a rate limit context. However, I forgot to address how this would relate to fault tolerance or failover strategies, which I sensed was a crucial aspect for Amazon's high-availability requirements. The interviewer probed further, asking about potential bottlenecks in the architecture, which exposed a gap in my approach.

The interview took a turn during the cost discussion. I highlighted the trade-offs of using in-memory versus persistent storage for rate limits. However, my estimation of operational costs seemed overly optimistic when I connected it back to hardware requirements for a real-world application. I could sense the interviewer was looking for deeper economic insight based on AWS services, but I couldn't provide concrete numbers, which felt like a setback.

The flow of the interview was generally positive, but I sensed some tension as I struggled with certain dimensions of the challenge. When I provided a final summary of the system, I felt I regained some confidence, but the interviewer pressed again on edge cases, implying that my approach lacked robustness. We ended the session with a few minutes left for me to ask questions, which I used to inquire about Amazon's current systems, but I remained aware that I hadn’t fully nailed required robustness.

Leaving the interview, I reflected on the areas where I could have improved, especially around distributed systems trade-offs and cost management. While I felt I had showcased my design mentality, I also recognized I lacked specific technical depth in certain areas that are evidently crucial for Amazon. The outcome was still pending, but I was realistic about my performance and what I might need to demonstrate in a follow-up, if offered one.