With its flexibility and scalability, Snowflake has become a hugely popular cloud data warehouse solution. However, its pay-per-use pricing model can also lead to unexpectedly high costs if usage is not optimized. In today’s budget-conscious environment, Snowflake cost optimization has become a priority for many organizations.
Reasons Why Companies Look For Snowflake Cost Optimization Tactics
Cost Reduction Initiatives
Many companies regularly look for areas to cut costs, either to improve profitability or to redirect budgets to other priorities. As a relatively new line item, Snowflake costs may come under scrutiny in cost reduction initiatives.
Unexpectedly High Snowflake Bills
Some companies may experience “bill shock” if their Snowflake usage exceeds original estimates. Getting Snowflake costs under control becomes imperative.
Usage Inefficiencies
Lack of query optimization, excessive storage, overprovisioned warehouses, etc can inflate Snowflake bills. Fixing inefficiencies saves money.
Growth Phase Is Over
Early on, companies may emphasize rapid prototyping and expansion on Snowflake. As growth slows, the focus shifts to optimization and cost management.
Budget Constraints
External factors like economic downturns may force tighter constraints on departmental budgets, requiring teams to maintain capabilities on lower budgets.
Low ROI
If the business value from Snowflake isn’t living up to the cost, companies look to cut costs or shift budgets to higher ROI areas.
Migration to Snowflake Complete
Migrating to Snowflake often involves upfront investments and legacy system overlap. With migration done, focus turns to cost optimization.
How Qrvey Helps Optimize Snowflake Costs
As SaaS applications increasingly adopt Snowflake for in-app analytics, many find themselves grappling with unexpectedly high data warehousing costs. The pay-per-query pricing model, combined with heavy usage from a fast-growing user base, can cause Snowflake bills to balloon rapidly. This is especially true when Snowflake is only being used for pre-built charts and dashboards.
Qrvey offers a cost-effective solution tailored to analytics in SaaS apps. Its embedded analytics layer enables the creation of fully custom dashboards. Meanwhile, the data management capabilities efficiently scale analytics data separate from the core operational data stored in Snowflake. With Qrvey, real-time Snowflake data can be blended in dashboards with synced data from Qrvey’s native data store. This optimized architecture reduces the queries hitting Snowflake, unlocking substantial cost savings. For product teams building in-app analytics, Qrvey delivers control over the analytics stack while proactively managing Snowflake costs.
