Why Your Snowflake Bill Keeps Climbing
Snowflake isn’t the problem — wasted data and poor governance are. Learn why mid-market firms overspend by 2–3x and how to cut costs by 40% without killing adoption.
Ali Z.
𝄪
CEO @ aztela
Table of Contents
Mid-size companies are waking up to $250k+ Snowflake, Redshift, Databricks, or BigQuery bills every year.
The CFO panics. The data team scrambles:
Tune queries
Resize clusters
Hunt down expensive jobs
Standardize logic
But here’s the truth: technical patches don’t solve cost problems.
Most of your bill isn’t queries or clusters.
It’s dashboards nobody opens, pipelines nobody trusts, and terabytes of “just-in-case” data you’ll never use.
The Real Problem: Strategy, Not Technology
Snowflake isn’t the problem.
Your lack of clarity and governance is.
Here’s what your invoice is actually paying for:
Millions to store and process data nobody uses.
Terabytes of “just-in-case” data hoarded for years.
Three teams building redundant pipelines to answer the same question.
Analysts running slow queries on messy, duplicated data.
CFOs thought they were buying “advanced analytics.”
What they got was a $250k support desk and metrics nobody trusts.
(Related: Why BI Projects Fail)
How Much Should Snowflake Really Cost?
For most mid-market firms (200–500 FTE), Snowflake spend should track adoption, not sprawl.
Company Size | Typical Data Volume | Healthy Snowflake Spend |
|---|---|---|
200 FTE | 5–10 TB | $100k–$150k / year |
500 FTE | 15–25 TB | $200k–$300k / year |
1000 FTE | 30–50 TB | $400k–$500k / year |
If your spend is 2–3x above these ranges, you don’t have a scale problem.
You have a governance and adoption problem.
Playbook: Cut Snowflake Costs by 40% Without Killing Adoption
1. Stop Paying for Useless Data
Ask one blunt question for every dataset:
“What P&L decision does this drive?”
If no clear answer → cut it.
Eliminate $5k/month commercial datasets nobody touches.
Require a business case for every new dataset.
2. Build a Single Source of Truth
Unify business definitions across departments.
End revenue debates between Finance and Sales.
Assign every metric an owner.
This prevents the “three redundant pipelines” problem that silently bloats costs.
3. Tie Every Dollar to an Outcome
No more vanity dashboards. No “quick pulls” without purpose.
Every project = clear, measurable business impact.
Examples:
“This reduced CAC by 12%.”
“This saved $50k in cloud spend.”
“This cut reconciliation time by 40 hours per month.”
(Related: How to Tie Data Projects to ROI)
4. Enforce Strategy and Governance
Governance isn’t red tape. It’s cost control.
Run quarterly usage reviews → what’s used, what isn’t, what gets cut.
Assign owners to every metric, dashboard, and pipeline.
Kill shadow IT before it hits the invoice.
Checklist: Snowflake Cost Optimization
Do This | Don’t Do This |
|---|---|
Tie data to business outcomes | Store “just-in-case” data for years |
Assign metric ownership | Let teams define revenue 3 different ways |
Review usage quarterly | Fund vanity dashboards nobody opens |
Cut unused datasets fast | Assume query tuning alone will save you |
The Bottom Line
Snowflake isn’t expensive.
Waste is expensive.
If your bill feels out of control:
Stop paying for data nobody uses.
Build a single source of truth.
Tie every dollar to business outcomes.
That’s how mid-market firms cut Snowflake costs by 40% in 90 days — without killing adoption.
Schedule a Data Strategy Assessment and learn how to bring your Snowflake spend back under control.
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