The in-memory data infrastructure landscape has undergone significant transformations since the Valkey fork, driven by shifts in governance, economics, and technical advancements. Initially, Redis's licensing model change prompted the Linux Foundation to launch Valkey, a Redis fork aimed at maintaining an open-source, BSD-licensed alternative. Valkey has made notable strides, such as introducing I/O threading to improve throughput for I/O-bound workloads while maintaining a single-threaded command execution model. Despite these advancements, the real architectural innovation is seen in systems like Dragonfly, which employs a multi-threaded, shared-nothing architecture to efficiently handle the demands of AI/ML workloads. Unlike Redis and Valkey, Dragonfly is built to leverage modern multi-core, large-memory machines, offering high throughput and simplifying operations without the need for complex sharding. This makes it particularly appealing for teams focusing on AI/ML-driven applications, as it delivers consistent performance under heavy data demands without the intricate scaling challenges associated with clustered architectures. Overall, while Valkey offers a cost-effective and open-source alternative to Redis, novel solutions like Dragonfly are setting new benchmarks by addressing the shortcomings of traditional architectures in the AI/ML era.