AI infrastructure is the physical and economic layer every AI request actually runs on: the chips, the memory, the network, and the power and cooling that turn electricity into tokens. Every agent reply and every Taskade Genesis app build is a request that travelled through a building like this and came back.
TL;DR: AI infrastructure is the hardware and facilities underneath every model call. A flagship AI rack draws about 120 kW against roughly 10 kW for a general-purpose one, which is why AI data centers use liquid cooling. A median text prompt uses about 0.24–0.34 watt-hours, and better than 99% of that leaves the building as heat. Build an app free →
Most AI vocabulary describes what models do. This category describes what they run on — the part that is measured in megawatts, litres per second, and dollars per million tokens rather than in benchmarks.
It matters for three practical reasons:
Cost.Inference cost is the dominant recurring expense of any AI product, and it is set by hardware properties like memory bandwidth, not by model quality.
Speed. How fast a reply streams is a tokens-per-second question, and that number is decided by memory, not arithmetic.
Limits. Context length, batch size, and concurrency all bottom out in physical constraints — how much high-bandwidth memory sits next to the chip, and how much heat the data center can remove.
None of it is machinery you operate. When you run AI agents, trigger automations, or sync data through 100+ integrations, you are renting slices of this stack by the request — which is precisely why it is worth a plain-English map.
The Stack, Top to Bottom
Every layer below the top one is invisible when you use a hosted AI product — which is the point of paying for a hosted AI product.
