Cache hit rate (CHR) is a commonly used metric in caching systems that indicates the percentage of requests successfully retrieved from the cache, but relying too heavily on it can obscure critical insights, particularly regarding cache miss rates (CMR). While high CHRs often suggest efficient performance, they can mask the severe implications of CMR spikes, which directly correlate with load spikes on databases, leading to potential system stress. Factors such as restarts during deployments, failed nodes, topology changes during scaling, and replication issues can all contribute to increased CMRs. Techniques like cache warming can mitigate these spikes by allowing new nodes to gradually acquire data before becoming fully operational. However, many caching services fail to adequately address these challenges, often attributing cache misses to customer errors rather than systemic issues. Companies like Momento focus on minimizing CMR spikes by using innovative solutions such as gRPC-backed messaging for rapid state change updates and by abstracting topology changes to ensure seamless cache performance without maintenance interruptions. This approach provides true elasticity and continuous availability, challenging traditional perceptions of cache performance metrics and emphasizing the importance of understanding and managing cache miss rates.