A common practice for protecting services offering APIs is rate limiting, which helps prevent bad actors from launching DOS attacks and enforces limits on the service. When implementing an HTTP API rate limiter, a central shared "state" is needed to manage multiple servers, typically achieved using Redis due to its prevalence in this use case. In our specific scenario, we need to limit concurrent open connections for a stateful interpreter/REPL, but unlike traditional time-based limits, our limit is based on the total number of open connections at any given time. To achieve this, each server stores and updates connection counts in Redis using unique IDs, aggregates counts for users across servers, and implements expiration times to handle failures, including refreshing keys every 3 minutes to prevent zombie "concurrent users".