01 The short version
Best option when the rest of the platform is already on AWS and the data gravity is real. Managed container services for the workload itself, with the platform's own metered AI service kept as a capped fallback rather than as the primary path.
- Most clients here already hold committed spend. Inference is placed to draw down commitments you are paying for anyway.
- Capacity is chosen against measured demand, not list price — the expensive mistake on AWS is GPU capacity you cannot keep busy.
- The platform's own managed model service can sit behind the gateway as a capped fallback, with no new vendor relationship.
02 What we do here
BUILD
Inference on AWS
Open-weight models running on AWS capacity, gateway routing in front, full cost and usage records underneath, the networking and IAM to hold it together. Deployed in your account with Terraform you keep.
RUN
Operate it monthly
Capacity planning against AWS quota and pricing, model rotations, incident response, and a monthly report that reconciles against your actual AWS invoice.
FOUNDATION
Accounts, network, IAM
If the AWS footprint does not exist yet, or exists as one account somebody made in 2021, we build the landing zone underneath before anything else goes on top.
03 The others
We are not loyal to any of them. The right platform is the one where your data already is, your latency is acceptable and the GPUs are actually available this quarter — and that answer changes.