Cloud Modernization for AI Workloads: What Actually Needs to Change

Put compute next to the data

Cross-region and cross-account data movement quietly dominates the bill and the latency budget for AI workloads. Co-locating processing with storage is usually the single highest-return change in an estate.

Make environments genuinely comparable

If development, staging, and production differ in data shape or permissions, model behaviour will differ too. Infrastructure as code plus representative, governed test data removes an entire class of production surprises.

Instrument cost per workload

Tag and attribute spend at the workload level so each use case carries its own running cost. Once teams can see cost per inference or per pipeline run, optimisation conversations become concrete.

• Separate training, batch inference, and serving costs.
• Set budget alerts per team, not only per account.
• Right-size scheduled jobs before adding autoscaling complexity.

Govern model access centrally

Approved providers, key management, logging, and retention policies belong in one place. Central governance is what lets individual teams move quickly without each one renegotiating risk.