THE SITUATION
Snowflake spend had grown 110% in 13 months. Spend had outpaced the value it was generating, and without a structured approach to consumption management, the trajectory was only going in one direction.
The consumption profile was heavily skewed toward compute. Storage made up the remainder. Neither was governed with the discipline the scale demanded, and there was no mechanism in place to change that.
THE SOLUTION
GrowthArc was brought in to close the gap between what the platform cost and what it delivered. The engagement was structured around a clear principle: consumption should be proportional to business value. Where it was not, it needed to be corrected.
Phase 1 focused on two areas where savings were quantifiable and achievable without organizational disruption: storage optimization and warehouse sizing. Sequencing mattered here. Quick, validated wins in production built the confidence needed to take on the more structurally complex compute challenges in subsequent phases.
WHAT WE BUILT
GrowthArc deployed its Cost & Performance Optimizer accelerator to identify unused data objects and surface high-churn tables accumulating retention overhead that served no active business purpose. Critically, the tool generated the data justification required for business sign-off before any action was taken, keeping accountability with the right stakeholders.
On the compute side, a Hybrid Recommendation Engine combining rule-based logic and AI analyzed utilization patterns across warehouses and translated them into specific sizing actions with estimated savings attached. Idle warehouse activity was mapped and used to enforce smarter auto-suspend policies, ensuring credits stopped the moment workloads completed.
THE OUTCOME
The engagement identified substantial annualized savings potential across storage and compute. Inactive storage emerged as the single largest opportunity, representing 20% to 27% of total storage spend, recoverable through structured lifecycle management alone.
Beyond cost recovery, the client now operates with a repeatable FinOps discipline. Storage decisions are governed by data. Warehouse sizing reflects workload reality rather than historical defaults. That shift in operating model is what makes the savings sustainable.
110%
Spend Growth Addressed
Monthly costs had doubled in 13 months. The engagement was designed to reverse that trajectory without compromising platform capability.
27%
Storage Spend Recovery
Inactive storage was the largest single savings lever, quantified and made actionable through structured lifecycle analysis.
9 Weeks
Phase 1 Delivered
Storage optimizations validated in production by week four. Warehouse re-sizing completed and validated by week nine.
FUTURE OUTLOOK
Phase 1 establishes the governance foundation. The next phase moves into workload profiling, query optimization, and the broader infrastructure of consumption control: budgets, tagging, and alerting. These address the compute-heavy profile at a structural level rather than at the surface.
With that foundation in place, the client is positioned to scale Snowflake adoption deliberately, with consumption growth tied to business outcomes rather than platform defaults.
