Data
90% cheaper in one click: Pairing Snowflake's elasticity with Sigma's visibility
03 September 2025 • 2 min read

A fast‑growing US-based insurance Managing General Agent (MGA) was expanding portfolios, partnerships, and regulatory obligations. Leadership knew that reactive spreadsheets and siloed systems wouldn’t scale, so they set a clear brief: tighten operations, unlock real‑time insight, and avoid a costly ground‑up rebuild.
The untapped savings in a ‘near‑real‑time’ pipeline
Snowflake’s pay‑as‑you‑go model delivers true flexibility, but that very flexibility makes configuration choices matter. Our near‑real‑time CDC (change data capture) pipeline pulled changes from an on‑prem SQL Server every five minutes via Azure Data Factory, landed them in blob storage, and Snowpipe streamed them into Snowflake. To guarantee fresh dashboards we dedicated a medium warehouse with a five‑minute auto‑suspend. Usage was entirely legitimate, yet the warehouse stayed up for most of each day, adding about $2,000 to the monthly bill.
Sigma spotlights the opportunity
Turning on Sigma’s out‑of‑the‑box Snowflake cost dashboards brought crystal‑clear visibility into hourly credit consumption. The charts showed a steady line of activity, even between file arrivals, highlighting a simple optimisation opportunity. A quick latency test confirmed that underwriting needed data within ten minutes, not millisecond freshness.
One‑minute timeout, 90 % savings
With that insight, we adjusted the warehouse’s auto‑suspend from five minutes to one. No code, no pipeline changes, just one configuration tweak that Snowflake’s architecture makes trivial. The next day the consumption graph dipped dramatically, and the month closed at $194 - a 90 % saving with zero impact on service levels.
Broader benefits for the MGA
- Operational headroom – budget and engineering cycles redirected to new underwriting features rather than cost reviews.
- Regulatory confidence – dependable data pipelines underpin timely, accurate reports for capacity providers and regulators.
- Future‑ready foundation – the same stack now powers Snowpark experiments, giving data scientists a clean runway for pricing and risk models.
Takeaway
Snowflake gives teams granular control over compute spend; Sigma makes those levers impossible to miss. Together they turn small configuration tweaks into six‑figure annual savings. Before planning a refactor, open the dashboard - the next big win might be one click away.
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