When the data platform bill grows faster than the business, the total tells you very little. We map consumption of queries, workloads, teams, and processes, then identify where performance and cost can be improved using evidence from your environment.
When it matters
The platform bill spiked and nobody can explain why.
Consumption increased, but business activity did not.
The platform has outgrown its original design.
Data volumes have increased, workloads have changed, queries have slowed, and infrastructure decisions made years ago are still driving today’s spend.
Finance wants the number explained.
Somebody has to defend the platform budget, but there is no reliable breakdown of how the spend is allocated.
What we build
How it runs
Diagnosis, 2 to 4 weeks, fixed price.
We analyze available consumption and query metadata to establish where platform spend comes from and which workloads are responsible.
Measurement pilot, 3 to 6 weeks, fixed price.
We apply selected changes to one workload and measure the before-and-after impact in your environment.
The project.
The remaining optimization scope, agreed upon based on the evidence produced during the pilot.
Frequently Asked Questions
Will this mean moving off Snowflake?
Usually not. Most optimization starts with how you use the existing platform: warehouse configuration, query patterns, scheduling, and workload behavior. We recommend moving a workload only when the evidence supports it.
How much can we save?
We do not publish a generic percentage. The answer depends on your workloads, consumption patterns, and architecture. We establish the baseline first and measure the opportunity in your environment.
Do you need access to the data in our production tables?
Usually not for the initial Diagnosis. Consumption and query metadata are often enough to identify cost drivers. We agree on the minimum access required before starting.