How HomeChef tamed Snowflake costs while raising cost awareness across their analytics team.
Home Chef is a meal kit and food delivery company that delivers pre-portioned ingredients and recipes to subscribers weekly in the United States. They deliver three million meals monthly to their subscribers.
Meal kit and food delivery
200-500
Chicago, United States
2004
Devin McGeeData Engineering Lead at Home Chef
Paul SchmidtData Engineer at Home Chef
Challenges
HomeChef had been using Snowflake for five years with stable costs, primarily powering daily batch analytics. As they added more operational workloads, costs spiked 15X. Their homegrown Looker dashboards used for cost monitoring solution worked for routine needs but struggled with new problems and required significant effort for ad-hoc analysis.
Solution
SELECT provided immediate cost visibility and optimization insights that HomeChef's internal tools didn't provide. Setup took just 30 minutes. SELECT's deep integration with dbt and comprehensive cost breakdown enabled the team to quickly identify high-impact optimization opportunities across their 1,200+ dbt models while providing the visibility they previously built custom dashboards to achieve.
Results
- Saved 60% through storage optimization and performance improvements identified via SELECT
- Shifted cost awareness upstream with analytics engineers proactively optimizing queries during development
- Eliminated time-intensive homegrown monitoring solution and manual cost analysis workflows
- Established daily cost monitoring routine that takes just 2-3 minutes to review entire Snowflake account
- SELECT continues to pay for itself through ongoing Automated Savings and continuously identifying new sources of waste
Snowflake costs exploded as HomeChef continued to add more operational workloads
HomeChef migrated from EMR to Snowflake five years ago, initially using it to fuel Looker dashboards with daily dbt batch updates, resulting in a stable and cost-effective setup.
Everything changed when HomeChef started using Snowflake to power more and more operational workloads.
“We went from running queries once a day to running queries every 15 minutes. When you do a lot of upserts on churn tables, Snowflake spend can move up very quickly. We had about an order of magnitude increase pretty much overnight”
Devin McGee
Data Engineering Lead at Home Chef
Homegrown monitoring hit its limits as complexity grew
Before SELECT, HomeChef built internal cost monitoring through incremental query history harvesting, warehouse segregation for dbt environment spend tracking, and custom Looker dashboards.
“We had all this roll-your-own stuff that was like a TEMU knockoff of SELECT that we had built internally”
Devin McGee
Data Engineering Lead at Home Chef
The homegrown solution worked for routine monitoring but became problematic when new issues arose. Adding features required PRing new dimensions into tables and measures into dashboards, while ad-hoc analysis was slow due to Snowflake's complex metadata structure.
“The inertia and friction is high. As the feature set that we use inside Snowflake expanded, you're effectively learning a new esoteric dataset each time.”
Devin McGee
Data Engineering Lead at Home Chef
Getting up and running with SELECT in 30 minutes
When costs spiked, HomeChef needed immediate visibility into what was driving spend. At that's where they looked to SELECT.
Within 30 minutes, they were able to get up & running with SELECT. SELECT's deep understanding of the dbt ecosystem and comprehensive cost visibility made the transition seamless, eliminating their need for homegrown solutions.
“SELECT feels like exactly what Paul and I would have built if we had locked ourselves in a room for 18 months to create our ideal monitoring solution.”
Devin McGee
Data Engineering Lead at Home Chef
SELECT enabled 60% savings through targeted optimizations
HomeChef achieved significant cost reductions by first addressing storage issues (saving 20%), then using SELECT to identify performance optimization opportunities worth an additional 40%.
With 1,200+ dbt models, identifying optimization targets manually would have been impossible. SELECT's cost breakdown and performance insights enabled the team to focus their efforts where they'd have maximum impact.
“SELECT served as both our target identification tool and our monitoring panopticon for performance optimizations. We had about 20 models that just throwing compute at was net savings”
Devin McGee
Data Engineering Lead at Home Chef
Daily monitoring creates proactive cost culture
SELECT transformed HomeChef's approach from reactive firefighting to proactive monitoring. Devin now starts every workday at with a quick SELECT review.
“I get my coffee, look at the pipes, then check SELECT. It takes me two to three minutes to get a sense of what's going on in our Snowflake setup every day”
Devin McGee
Data Engineering Lead at Home Chef
This routine enables early detection of anomalies, new workloads, and cost spikes before they become major issues.
“I can see changes in our usage over time and it lets us know if there's new use cases we haven't anticipated, which lets the business analytics team work with those end users directly”
Devin McGee
Data Engineering Lead at Home Chef
Cost awareness shifted upstream to development teams
Perhaps the most significant cultural change was analytics engineers beginning to consider optimization during development rather than after deployment.
Through pairing sessions with SELECT insights, the broader team learned query optimization principles. Now analytics engineers proactively optimize performance before submitting pull requests.
“The AEs now will front-load 'hey, this is going to be an expensive one' and will say 'I got the runtime on this down from X to Y prior to putting in the PR because I knew this window function was going to kill me”
Devin McGee
Data Engineering Lead at Home Chef
Equally important, SELECT helps guide when not to optimize, preventing wasted effort on low-impact models.
“Being able to quickly say 'don't worry about that one, it's going to be $200 or $300 a year' - it's not worth you blowing a day worrying about it”
Devin McGee
Data Engineering Lead at Home Chef
Insights feature provides ongoing optimization opportunities
With their costs stable, Home Chef shifted to ongoing monitoring and periodic review of optimization opportunities to identify potential low hanging fruit.
“The Insights feature is really nice - you basically have free labor identifying things that are highest impact ROI, 80-20 kind of thing. We use it quarterly to go through the list and see if anything jumps out that's worth a few hours to save a couple grand”
Paul Schmidt
Data Engineer at Home Chef
This systematic approach ensures ongoing optimization without constant manual analysis.
Looking ahead with SELECT
HomeChef plans to explore SELECT's Views feature to streamline their daily monitoring routine. Devin is interested in creating saved views for new workloads, query patterns, and long-running queries to make his morning checks even more efficient.
As HomeChef's Snowflake infrastructure becomes increasingly critical to business operations while maintaining flat costs, SELECT remains essential for balancing growth with cost control.
“Our Snowflake infrastructure is much more important to the business broadly than it was 18 months ago, and our spend is pretty much flat from when we started streaming. SELECT's been a really important part of that process”
Devin McGee
Data Engineering Lead at Home Chef
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