Insight
7/4/25
Snowflake vs Databricks vs BigQuery (2025 Comparison Guide)
Compare Snowflake, Databricks, and BigQuery in 2025. See costs, strengths, and tradeoffs — and learn why strategy matters more than vendor features.
Snowflake vs Databricks vs BigQuery: Why This Question Matters in 2025
Every week, I talk to CFOs and CTOs spending $300k+ a year on data platforms.
The first question they ask? “Which is better: Snowflake, Databricks, or BigQuery?”
It’s a fair question. But here’s the blunt truth: starting with vendor choice is the wrong starting point.
Still, it’s worth comparing these three giants — because they dominate mid-market and enterprise decisions.
Snowflake: The Data Warehouse Standard
Strengths:
Best-in-class for simplicity and usability.
Flexible compute scaling with pay-as-you-go pricing (per credit model).
Huge partner ecosystem + broad adoption in mid-market firms.
Weaknesses:
Costs can skyrocket without governance (unused dashboards, data hoarding).
Primarily SQL + BI workloads — less native ML/AI flexibility.
Best Fit:
Firms with lean data teams needing a straightforward warehouse for BI, reporting, and scaling analytics.
👉 Related reading: Why your Snowflake bill is so high
Databricks: The Data + AI Platform
Strengths:
Built for machine learning and AI workloads.
Strong in data lakehouse architecture (structured + unstructured).
Great for organizations with heavy data science investment.
Weaknesses:
Steeper learning curve vs Snowflake or BigQuery.
Can be overkill for mid-size firms that just need BI.
Best Fit:
Firms with mature data science teams that want ML + AI integrated into their platform.
BigQuery: Google’s Serverless Option
Strengths:
Fully serverless — no infrastructure management.
Tight integration with Google Cloud + AI services.
Competitive pricing for query-heavy workloads.
Weaknesses:
Vendor lock-in risk if you’re not already in Google Cloud.
Less user-friendly for non-technical teams compared to Snowflake.
Best Fit:
Firms already invested in Google Cloud looking for low-maintenance analytics.
Comparison Table: Snowflake vs Databricks vs BigQuery (2025)
Feature | Snowflake | Databricks | BigQuery |
|---|---|---|---|
Primary Strength | Data warehouse simplicity | ML + AI platform | Serverless analytics |
Best For | BI, dashboards, mid-market | AI/ML-heavy orgs | Query-heavy workloads |
Ease of Use | High | Medium | High (but technical) |
Cost Model | Pay per credit (compute + storage) | Usage-based, can be complex | Query-based pricing |
Adoption Risk | Costs explode without governance | Overkill for smaller teams | Lock-in to Google Cloud |
The Fatal Mistake: Vendor-First Thinking
Comparing Snowflake, Databricks, and BigQuery is useful — but it won’t give you ROI if you skip the strategy step.
The mistake:
Engineers run vendor demos.
Vendors oversell features.
CFO signs a $300k+ deal.
Six months later → adoption is <10%, trust is lower than before.
👉 The right way:
Define the P&L problem you’re solving.
Agree on the minimum viable data model (10–15 metrics that matter).
Only then decide which platform gets you there fastest and simplest.
The Bottom Line
Snowflake, Databricks, and BigQuery are all powerful.
But choosing between them without a clear strategy is how companies waste $300k+ on shelfware.
So yes, compare features and pricing.
But only after you’ve answered:
“What’s the P&L problem we’re solving?”
“What’s the minimum viable data model?”
The tool should be the last decision, not the first.
👉 Next: Why Choosing a Data Platform First Is a $300k Mistake
