Comparison
Snowflake vs BigQuery: how to choose a cloud data warehouse
Snowflake and Google BigQuery compared for data leaders: architecture, clouds, pricing models (warehouses vs bytes scanned and slots), performance and concurrency, data sharing, AI features and when each fits.
Hemant Walchale
· 2 min read
Snowflake and BigQuery are both fully managed, SQL-first cloud data warehouses that separate storage from compute. Either can run a modern analytics platform. The decision usually comes down to your cloud, your cost profile and your ecosystem.
The short answer
- Choose BigQuery if you are committed to Google Cloud, lean on Google Analytics, Google Ads and Looker, and want a truly serverless warehouse with nothing to size.
- Choose Snowflake if you run on AWS or Azure or several clouds, want to control compute per workload, or share data heavily with partners and customers.
How they compare
| Snowflake | BigQuery | |
|---|---|---|
| Clouds | AWS, Azure, Google Cloud | Google Cloud (Omni for limited cross-cloud) |
| Compute model | Virtual warehouses you size and suspend | Serverless; on-demand or slot reservations |
| Pricing | Credits for warehouse running time, plus storage | Bytes scanned (on-demand) or slot capacity (editions), plus storage |
| Workload isolation | Separate warehouses per team or workload | Reservations and assignments |
| Semi-structured data | VARIANT, OBJECT, ARRAY |
Native STRUCT and ARRAY, nested and repeated fields |
| Data sharing | Secure data sharing, Marketplace, clean rooms | Analytics Hub, data clean rooms |
| AI and ML | Cortex AI functions, Snowpark | BigQuery ML, Vertex AI integration |
| Ecosystem strengths | Neutral across clouds and BI tools | Google Analytics 4 export, Google Ads, Looker |
| Open table formats | Apache Iceberg tables | BigLake tables with Iceberg |
What usually decides it
- Your cloud. BigQuery is strongest inside Google Cloud; Snowflake avoids cross-cloud data movement elsewhere.
- Cost profile. Ad-hoc, infrequent querying often favours BigQuery on-demand; steady, heavy or highly concurrent workloads need modelling on both. Many cost surprises on BigQuery come from unfiltered scans of large tables, and on Snowflake from warehouses left running.
- Marketing data. A Google Analytics- and Google Ads-centred stack is simpler on BigQuery.
- Data sharing. Frequent sharing with partners and customers favours Snowflake.
- Operations preference. BigQuery has less to configure; Snowflake gives more explicit control.
The short version
Google Cloud-committed and marketing-heavy points to BigQuery; multi-cloud, workload isolation and data sharing point to Snowflake. Model your heaviest workloads on both pricing models before deciding.
Moving between them? See BigQuery to Snowflake. Model Snowflake costs with the cost calculator, or compare all platforms with the platform chooser.
Not sure where your own platform stands? Take the free 3-minute data platform scorecard, or see how we work on Snowflake. Unfamiliar terms are explained in the data engineering glossary.