Find what is driving your cloud bill before cutting capacity
If AWS, Azure or GCP spend is increasing faster than the product or team, we review the cost and usage data alongside the architecture that creates it. The goal is to separate low-risk waste from changes that need engineering judgment.
What we examine
We start with billing and usage data, then trace the largest or least explainable cost drivers back to the workloads and architecture responsible.
- Cost allocation by account, subscription, project, team or environment
- Idle and underused compute, databases, storage and supporting services
- Sizing decisions that no longer match observed usage
- Data transfer, replication and storage patterns creating recurring cost
- Commitment coverage where baseline usage is stable enough to justify it
- Tagging, budgets and ownership controls that prevent cost from becoming invisible again
What we will not promise before measurement
We do not quote a percentage saving before seeing the estate. A lower bill is not an improvement if it removes required headroom, resilience or operational controls.
Useful inputs include a recent billing export, billing-console access, architecture context and someone who can explain unusual workloads or seasonal demand.
What you receive
A current-state cost map tied to the architecture that creates the spend
Prioritized findings with implementation effort, operational risk and dependencies
Rightsizing and cleanup opportunities that can be validated before production changes
Commitment analysis based on demonstrated usage rather than a blanket purchase target
A short list of architectural changes worth investigating separately