A practical framework for enterprise AI procurement
Map model access, cloud capacity, data infrastructure and developer tools before choosing vendors.
Enterprise AI procurement is not a single model decision. A production AI service usually combines model access, compute, data, observability, security and developer tooling—each with different commercial and operating constraints.
Start with the workload
Define what the application must do, where it will operate and how demand may change. This keeps model evaluation connected to latency, data residency, availability and cost requirements.
- Expected request patterns and peak demand
- Supported data types and sensitivity
- Required markets and user locations
- Evaluation, pilot and production stages
Map the full stack
Evaluate the model together with the infrastructure around it. Ownership gaps between model, cloud and data services often become billing, renewal or incident-response gaps later.
Create a path to production
Separate experimentation from production governance. Assign owners for usage visibility, commercial approval, renewal review, vendor changes and fallback options before volume grows.