Case studies / Data foundations
Data foundations · Grocery retail
Cutting $50K a month from a Snowflake bill — without losing performance
Snowflake spend had grown to the point of real budget pressure, with no clear picture of where the cost was going — and any change risked hurting query performance. We found $50K a month, without giving performance back.
Recurring monthly savings on Snowflake spend.
Query performance held steady throughout.
No degradation to the reporting experience end users felt.
The challenge
Rising cost, unclear cause, real performance risk
Snowflake spend had grown to the point of real budget pressure, with no clear picture of where the cost was going — and every proposed fix carried a risk of hurting query performance or user experience.
Before
- Snowflake spend rising with no clear driver
- No workload-level cost visibility
- Any change risked hurting performance
- Cost conversations stalled on uncertainty
After
- $50K/month saved, recurring
- Performance held steady on critical workloads
- User experience unaffected
- Clear, ongoing visibility into spend drivers
How we approached it
Audit first, then tune without disruption
Right-size warehouses
We eliminated over-provisioning while protecting the reporting SLAs and user experience that mattered.
Tune workload policies
We set auto-suspend and auto-resume policies separately for batch workloads and office-hours warehouses.
Reduce storage overhead
We compressed large transaction and log tables to cut storage cost without losing the data teams needed.
Match retention to recovery need
We tuned time-travel and fail-safe retention windows against the client's actual recovery requirements.
Under the hood
Next step
Watching a cloud data bill climb with no clear reason why?
We find where the cost is really going, then bring it down without touching the performance people depend on.