Scale your workloads without paying all-RAM prices

Rising demand for AI infrastructure has increased DRAM costs and constrained supply, creating challenges for organizations that rely on in-memory databases to support growing datasets, real-time decision systems, caches, feature stores, and AI agents. Reducing retained data or shifting more requests to slower systems can lower memory spending but may weaken application performance and decision quality, particularly for high-volume fraud, recommendation, and agentic workloads. The proposed approach is storage tiering, in which frequently accessed data remains in RAM while less active keys and values are stored on lower-cost SSDs and loaded into memory when needed, allowing applications to retain a single database interface. Redis presents Redis Flex as an implementation of this model, offering configurable RAM-to-SSD ratios, claimed lower per-gigabyte costs, no required code changes, and preview support for Redis Search indexes stored on SSD.

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