The problem
What feels instant at demo scale changes under real traffic.
A prototype answers quickly because almost nothing is running through it. Under real traffic, the same model and retrieval calls repeat constantly — and latency and cost climb with every one of them. The behavior that felt instant in a demo is exactly the behavior that gets expensive at scale.
Caching is what keeps a promising demo feeling the same once people are actually using it.
How it works
Cache the work that repeats, at the layers where it repeats.
AI Infra caches the work that repeats — model responses and retrieval results — at the layers where repetition happens. Identical calls are served from cache instead of being recomputed, so latency stays steady as usage grows and model spend tracks real demand rather than repeated identical work.
It's part of the runtime, not an add-on the product team has to build and tune separately — so an application is ready for real traffic from the start.
- Model responses are cached so repeated work isn't paid for twice
- Retrieval results are cached so grounding stays fast under load
- Latency stays steady as traffic grows, instead of climbing with it
- Model spend tracks real demand rather than repeated identical calls
- Built into the runtime, so the product gets it without extra plumbing
Ready for real traffic.
AI Infra is one of the accelerators our Services team builds on. Tell us what you're building and we'll stand it up on a runtime that's ready for production.