Every time you enter a short prompt into your enterprise AI tool, behind that pulsing indicator you see on the screen, a flurry of invisible mechanisms and processes goes to work in the infrastructure. As the tool attaches the enterprise policies, session history, retrieved documents, and all the other contextual data it needs to generate a useful and reliable output, the dozen words you’ve typed systematically become a 40,000-token workload.
Now consider that thousands of users and agents may be hitting the system at once, each prompt reaching into a corporate knowledge base that, at the world’s largest companies, can run to 100 petabytes (PB). Serving from that archive generates a second active store, the key-value (KV) cache, that scales not with how much data you have but with how many users are querying it at the same time.
The expensive computation over those tokens becomes a cache worth saving and reusing to prevent redundancies and inefficiencies that bottleneck output. But the tool can only do that if the technical infrastructure has somewhere to keep that cache. And many enterprises don’t consider the need for this storage when they’re building AI infrastructure—until they run out of room.
