The Cloud Cost Optimization Sprint: 45% Reduction in 8 Weeks

This company was burning six figures a year on cloud data - we cut it by 45% in 8 weeks.

Snowflake, Databricks, and AWS. Three platforms, dozens of workloads, zero cost visibility. The CFO saw the monthly bill climbing and asked a simple question: “What are we actually paying for?”

Nobody could answer it.

Week 1-2: Full cost audit. Storage, compute, data transfer - line by line. We found 3TB of data nobody had queried in 6 months. Warehouses running 24/7 for workloads that ran twice a day.

Week 3-4: Storage fixes. Tiering cold data, enabling compression, setting retention policies. Quick wins alone covered a significant chunk of the total savings.

Week 5-6: Compute right-sizing. Auto-suspend after 5 minutes instead of 30. Smaller warehouses for development. Scheduling production jobs in off-peak windows.

Week 7-8: Pipeline optimization. Switching full refreshes to incremental loads. Better partitioning. Query result caching for repeated analytics.

Result: a 45% reduction. Nearly half their annual spend back in the budget.

The uncomfortable part? Most of these were things someone should have caught 12 months earlier. Nobody was looking.

What’s the last time someone audited your cloud data spend line by line?