AWS vs. Google Cloud vs. Azure for an AI product: how to choose

The three big clouds can all run a serious AI product. The right choice depends less on marketing and more on your data, your team, and your appetite for lock-in.

Start from your data

Wherever your data already lives is a strong default, because moving data is slow and expensive. If you are on Google Workspace and BigQuery, Google Cloud is a natural fit; if your enterprise runs on Microsoft, Azure reduces friction; if you are already deep in AWS, its breadth is hard to beat.

Models and services

All three offer managed model hosting and first-party and partner models. What differs is the surrounding ecosystem: data warehousing, vector search, MLOps tooling, and how easily they connect to the rest of your stack.

Cost and lock-in

Pricing is comparable at a high level but diverges by workload. The bigger long-term cost is lock-in. Building on proprietary services is faster today and harder to leave tomorrow.

Our approach

We build cloud and model agnostic wherever it is sensible, so you keep leverage. That means portable infrastructure as code, an AI gateway that lets you swap providers, and choices made for your product, not our convenience. See our cloud and DevOps service.