Infrastructure & Deployment
Where your models run determines your cost, your latency, and — often decisively — your privacy posture.
Cloud Computing for AI
Cloud gives you immediate access to the largest models and effectively unlimited scale, with no hardware to buy. The tradeoffs are per-call cost that grows with usage, network round-trip latency, and the fact that your data leaves your premises. For most small and mid-sized organizations cloud is the right default — but the privacy question has to be answered explicitly, in writing, before sensitive data flows.
Edge Computing for AI
Edge means the model runs on the device — a phone, a camera, a machine on the floor. It is fast, works without connectivity, and the data never leaves the device, which resolves whole categories of privacy and compliance concern. The constraint is compute: edge models are smaller and less capable, and updating them across a fleet is real operational work.
Hybrid Architectures
In practice most mature deployments are hybrid. Sensitive or latency-critical processing happens locally; heavy reasoning and aggregate analysis go to the cloud. A common pattern: strip or tokenize identifying data at the edge, send the sanitized remainder to a large cloud model, and reassemble locally. You get frontier capability without frontier exposure.
GPUs and TPUs
AI math is massively parallel, which is why graphics processors run it hundreds of times faster than general-purpose CPUs. TPUs are Google's purpose-built equivalent. Unless you're training or self-hosting, you don't buy these — you rent them inside the price of every API call. What matters to you is that GPU scarcity is the reason inference costs what it does, and why smaller models are often the smarter commercial choice.
Decide the privacy requirement first — it constrains everything else. Then optimize for latency and cost within that boundary.