Spend moves fast. Governance usually arrives late.
Cloud and AI/API bills often grow before ownership, tagging, budgets, and alerting mature. The result is surprise invoices, unclear accountability, unused resources, and slow decisions between finance and engineering.
Cloud usage expands across teams, accounts, subscriptions, and experiments without a clear owner.
Resources exist without reliable product, team, environment, or customer attribution.
API and model usage grows outside the same controls used for cloud infrastructure.
A focused 7-14 day sprint.
Digiscience reviews the current cloud and AI spend operating model, identifies waste and governance gaps, and delivers a prioritized action plan with quick wins and implementation options.
- Cloud spend and waste analysis.
- Tagging and cost ownership review.
- Budget, alert, and anomaly detection recommendations.
- Rightsizing and cleanup opportunity list.
- IAM/governance quick-risk review.
- AI/API usage governance checklist where applicable.
- Executive report with a 30-day action plan.
Best fit
Active AWS, Azure, or hybrid cloud footprint with growing engineering delivery.
Enough moving parts that cost ownership and deployment guardrails matter.
Cost surprises, ownerless resources, budget gaps, tagging drift, or AI/API usage growth.
No for the first call. For the sprint, read-only billing, cost, tagging, and selected cloud inventory access is usually enough.
No. The sprint supports AWS, Azure, and hybrid environments. The first version is strongest for AWS/Azure SaaS and technology teams.
The sprint is designed to improve cost visibility and guardrails without blocking delivery.
Yes. The audit can convert into a focused sprint for budgets, alerts, tagging cleanup, rightsizing, and operating rhythm.