Your data is growing. Your RAM bill doesn't have to.

AI is pushing memory prices up, fast

For decades, memory got cheaper over time. In late 2025 that reversed, and AI is the reason. Memory makers have shifted their factories toward the high-bandwidth memory that AI accelerators need, leaving less capacity for the ordinary server DRAM everyone else depends on, while the biggest cloud providers lock up what's left with long-term deals.

The result is the fastest memory price jump on record. Server memory prices roughly doubled in early 2026 and kept climbing, and new factories won't bring relief until 2027 at the earliest.

If you run an in-memory database, this hits you directly. RAM you already own doesn't get pricier, but adding capacity, duplicating it for reliability, or reserving it for next year's growth now costs more. And most of that RAM holds data that's rarely touched. In almost every app, a small slice of the data handles most of the traffic. When everything’s in memory, you're paying top dollar for the quiet majority to sit next to the busy minority.

Storing less isn't a real answer

To fit the RAM budget, teams keep less history, shrink the cache, or push data to other systems. Each has a price. Shorter retention puts useful history out of reach. A smaller cache sends more traffic to your main database, which is slower and costlier per query. Spreading data across systems adds lookups and more to maintain.

Fraud and recommendation systems feel this most. They make hundreds of thousands of decisions per second with a few milliseconds each, and every decision is only as good as the features you can afford to serve. What teams need is a way to keep more data at real-time speeds without paying RAM prices for all of it.

Not all tiering is created equal

The natural fix is to pair RAM with SSD: hot data in memory, the rest on SSD at a fraction of the cost per gigabyte. But how a system does that decides how much you actually save.

Some systems, including Redis's earlier Auto Tiering, move values to SSD but keep every key in RAM. That eats memory before you've stored any hot data, and it gets worse as record counts grow. Others only start using SSD once memory is full, so a small test looks blazing fast and production is where you find the slowdowns.

A system that moves both keys and values to SSD leaves RAM for the data your app touches constantly. When a request comes in, RAM is checked first. If the data is on SSD, it's pulled into RAM, served, and kept there while it stays busy. Your application sees one database and one API, and older data is there when you need it.

The right ratio depends on the workload. A huge dataset with a small hot slice can run on a low RAM share; one that touches most of its data needs more. If every request must return in under a millisecond, all-RAM is still the right call. The point is that capacity becomes a dial you control rather than a bill you absorb.

Put this into practice

Dial in your RAM and SSD ratio so you can run terabyte-scale datasets without keeping everything in memory.

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Make room for richer features and longer history

With hot data in RAM and the rest on SSD, you stop cutting history, shrinking caches, or splitting data across systems. You just keep it.

The fraud and recommendation systems that feel the squeeze hardest get the most back: longer customer history, more signals per decision, and no lost milliseconds fetching them. A cache can serve more requests without going back to the primary database. And an AI agent can draw on earlier interactions and account activity without stitching that context together from scattered systems.

Put this into practice with Redis Flex

Redis Flex combines RAM and SSD so you can run terabyte-scale datasets without keeping everything in memory. It keeps hot data in RAM and moves less frequently used keys and values to SSD. You choose a RAM ratio from 10% to 50% per workload and change it as your needs or memory prices change. At the SSD-heavy end, Flex costs up to 80% less per gigabyte than RAM alone, with the same Redis you already use and no code changes. Flex now supports Redis Search too, with large indexes stored on SSD (in preview on Redis Cloud Pro).

If you're growing a feature store, expanding a cache, or giving your AI agents more to remember, Flex lets you keep more useful data while buying less of the most expensive resource in the data center.

Talk with a solutions architect about using Redis Flex to meet your data growth, performance, and cost goals.