How We Saved a Client $180K/Year on Snowflake Costs

The Challenge: Runaway Snowflake Costs

When a Series B SaaS company reached out to us in early 2025, they were spending approximately $24,000 per month on Snowflake—nearly $290,000 annually. For a company with 200 employees and $25M in ARR, this represented 1.2% of revenue, well above the industry benchmark of 0.3-0.5% for companies their size. The finance team was demanding cost reductions, but the data team didn't know where to start without breaking critical dashboards and analytics workflows.

The company's data stack included Snowflake as their warehouse, Fivetran for data ingestion, dbt for transformations, and Looker for business intelligence. They had roughly 2TB of data, 150+ dbt models, and 40 Looker dashboards serving business users across sales, marketing, and customer success teams.

The Discovery: Where $24K/Month Was Going

Our Cloud Cost Optimization sprint began with a comprehensive analysis of their Snowflake usage patterns. Using query history, warehouse utilization metrics, and storage analysis, we uncovered the primary cost drivers:

• Oversized warehouses (35% of costs): The production warehouse ran 24/7 on an X-Large size, even though 70% of queries could complete efficiently on a Medium
• Inefficient dbt models (28% of costs): 12 incremental models were performing full table scans on every run due to missing clustering keys and poor filter logic
• Dashboard query patterns (18% of costs): Looker dashboards were hitting Snowflake directly for every page load, with no caching or aggregation layers
• Data retention (12% of costs): Raw event data from the past 3 years sat in Snowflake, despite business requirements only needing 13 months
• Development waste (7% of costs): Development and staging warehouses ran continuously, including nights and weekends when no one was working

The Solution: A Multi-Layered Optimization Approach

We implemented a phased optimization plan over four weeks, starting with low-risk, high-impact changes. First, we right-sized warehouses using Snowflake's query performance data, moving the production warehouse to a Large size with auto-suspend set to 1 minute. We created separate warehouses for different workload types (BI, dbt, data science) with appropriate sizing. This single change reduced compute costs by $6,800 per month.

Next, we optimized the 12 most expensive dbt models by adding clustering keys, rewriting CTEs to reduce intermediate result sets, converting several incremental models to use merge optimization, and implementing proper incremental logic with lookback windows. These model improvements reduced transformation costs by $4,200 monthly and cut dbt run times from 45 minutes to 18 minutes.

For the BI layer, we implemented a materialized aggregation strategy, creating summary tables for common dashboard queries and setting up automatic refresh schedules. We also enabled Looker's PDT caching for expensive explores. This reduced BI query costs by $3,600 per month while actually improving dashboard load times by 70%.

The Results: 62% Cost Reduction

After implementing all optimizations, the client's Snowflake costs dropped from $24,000 to $9,100 per month—a reduction of $14,900 monthly or approximately $180,000 annually. This 62% cost reduction was achieved with zero degradation in performance or functionality. In fact, dbt runs completed 60% faster, and dashboard load times improved significantly due to the aggregation layer.

The breakdown of savings by category: warehouse right-sizing saved $6,800/month, dbt optimization saved $4,200/month, BI aggregations saved $3,600/month, storage lifecycle policies saved $1,900/month, and automated scheduling of non-production warehouses saved $1,400/month. The initial investment in the optimization sprint paid for itself in less than one week.

Sustaining the Savings: FinOps Culture Change

Cost optimization isn't a one-time project—it requires ongoing attention. We helped the client establish sustainable FinOps practices including weekly cost review meetings, Slack alerts for unusual spending patterns, query cost visibility in dbt Cloud, and monthly optimization sprints to address new inefficiencies. We also trained their data team on cost-efficient SQL patterns and Snowflake optimization techniques.

Six months after our engagement, the client's costs have remained stable at approximately $9,500 per month despite 40% data growth and 10 new dbt models. The data team now considers cost implications during design reviews, and they've caught several potentially expensive patterns before they reached production.

Your Snowflake Costs Could Be Next

This case study demonstrates what's possible with focused, expert optimization. If your Snowflake (or Databricks, BigQuery, or Redshift) costs feel out of control, you're not alone—and you don't have to accept them. The Big Data Company's Cloud Cost Optimization sprint delivers similar results for data teams across industries. For $2,490, we'll analyze your environment, identify savings opportunities, and implement high-impact optimizations that typically generate 10-20x ROI. Ready to cut your cloud data costs by 40-60%? Let's start with a conversation about your current challenges.