How to forecast cloud costs on AWS, Azure and Google Cloud: methods, variance targets, tool limits, AI workload pitfalls and a rolling process you can run.
AWS tagging best practices for cost allocation and governance: a lean schema, tag policies, SCPs, AWS Config, activation pitfalls and multi-cloud notes.
Prioritize idle instances and nonproduction schedules, then resize databases, tune storage, and commit when usage stabilizes. Use the runbook for 90 days.
Set up a GCP billing export to BigQuery, avoid dataset location and backfill mistakes, query costs, account for GKE, and keep FinOps workflows on track.
Control SageMaker costs with a 30/60/90 day plan: track spend, checkpoint Spot training, match inference to demand, and commit only when usage is predictable.