For many teams, Snowflake's data warehousing capabilities seem limitless at first.
But as usage grows more sophisticated, costs can balloon if not kept carefully in check. When Snowflake bills start arriving much larger than expected, it's time to tame the proverbial blizzard before it buries the budget.
This guide focuses on twelve common culprits that can cause Snowflake costs to rise unexpectedly — and a practical solution specific to analytics.
From inefficient queries to oversized warehouses to unexpected data growth, these cost creep issues often happen gradually beneath the surface. But their collective impact can add up to a budget-busting blizzard if not addressed.
The twelve Pitfalls.
01Unoptimized Queries
Inefficient query writing can result in suboptimal queries that consume excessive credits. This is a major cause of unexpected cost spikes.
02Over-Provisioned Warehouses
Oversized virtual warehouses that aren't scaled back appropriately can rack up costs, especially for idle time.
You have to balance warehouse size and return latency. At some point, you end up paying more for the same latency.
03Data Volume Growth
More data means higher storage costs.
Unexpected data volume spikes from new sources can drive up costs. This is especially true in embedded analytics use cases for growing SaaS companies.
04Complex Workloads
Highly complex analytic workloads require more processing power and can exponentially increase compute costs.
Similar to previous pitfalls, you have to strike a balance — testing and iterating is key to optimizing complexity.
05New Use Cases
Adding new analytics use cases without planning for associated costs can surprise you with higher bills.
Another reason SaaS companies struggle with Snowflake for embedded analytics: custom reports can mean suboptimal query loads.
06Using it with Embedded Analytics
You started with an internal need to analyze data, but decided you could use it for both internal and external use cases.
Embedded analytics use means constant usage. Your tenants will use your reporting (and querying) throughout the entire day across time zones.
07User Growth
Broader adoption leads to greater overall usage and credits consumed.
Adoption is generally a good problem to have — but alongside Pitfall #6, it means constant credit usage by SaaS users.
08Infrastructure Issues
Technical issues like warehouse failures requiring re-processing of workloads burn extra credits.
When this happens for an entire SaaS platform's worth of data, it can spell big expenses.
09Third-Party Integrations
New third-party tools and connections can introduce additional fees that raise costs.
This is beyond query usage alone — it also raises ETL and ingestion credit usage that may not be necessary for a typical SaaS platform data lake.
10Duplicate or Stale Data
Retaining excessive duplicate or unnecessary data inflates storage bills.
SaaS platforms should have a data retention policy — but if Snowflake starts with internal usage, those SLAs may not be in place.
11Permission Creep
Adding users or loosening role permissions expands potential usage and costs.
This is a big factor in embedded analytics: your customers will keep giving their own users access to custom reporting capabilities.
12Lack of Monitoring
Not monitoring usage and billing metrics hides inefficient spending.
It's always important to stay on top of spending and usage. When you're running it inside a SaaS application, this becomes part of your margin calculation, so keep a watchful eye.
How Qrvey helps lower your Snowflake bill.
Qrvey's analytics solution lets you mix data on one dashboard from multiple sources — so you can query Snowflake less frequently.
By mixing data sources, you isolate your Snowflake usage to real-time data.
Syncing your non-real-time data to Qrvey means cost-effective data queries — without changing what your customers see in your product.
Ready to find out how much you could save?
Try our Snowflake Savings Calculator to see what you could be saving on your Snowflake bill.
Calculate your savings →