The ROI timeline: hour one, week one, quarter one
Caching ROI isn't a hockey stick, it's a staircase that starts the first hour. Here's the honest schedule of when each saving shows up.
Hour one: deployment and first hits. The container is up in five minutes; the first semantic hit lands as soon as any question repeats, in support and docs workloads, usually within the hour. The dashboard's saved counter starts moving the same afternoon. This is the demo-to-yourself phase, and it costs nothing but the afternoon.
Week one: the head of your distribution warms. Hit rate climbs as the corpus accumulates your canonical questions; latency percentiles visibly split into instant-hits and normal-misses. This is when the top-misses view starts directing pre-warming and when someone screenshots the savings number into Slack.
- 1'how do refunds work?'
- 2model call · $$ · 2s
- 3'what's the refund window?'
- 4model call · $$ · 2s
- 5'refund timeline?'
- 6model call · $$ · 2s
- 7the same answer, three times
Every paraphrase is a fresh bill, unless the cache understands meaning.
Quarter one: the structural effects arrive. Budgets and keys turn AI spend into a governed system; a model upgrade happens without a cold start; the per-query blended cost in your unit economics quietly drops a tier and stays there. The cache stops being a project and becomes a number everyone assumes.
The bottom line
The honest caveat: novel-heavy workloads climb the staircase slower, and Replay will tell you that in advance, free. For everyone else, the schedule above is boringly reliable, which is the best thing a cost curve can be.