BigQuery and Snowflake are the two most widely deployed cloud data warehouses. Both are mature, performant, and well-supported. The decision comes down to your cloud platform, pricing tolerance, SQL dialect requirements, and ML/AI ambitions.
The quick answer
BigQuery and Snowflake are both mature, cloud-native data warehouses with strong SQL analytics capabilities. The short version: BigQuery is the natural choice for GCP-first organisations and those with Google Workspace or significant Google ecosystem investment; Snowflake is the natural choice for multi-cloud organisations, those that need the most mature SQL warehouse feature set, or those where storage-compute separation and flexible scaling are priorities.
Neither is categorically better. Both perform well on typical analytics workloads. Both support dbt, Tableau, Power BI, and Looker natively. The decision at most organisations is driven by cloud platform preference, existing ecosystem commitments, and pricing model tolerance — not by a meaningful technical gap.
What each platform is
### BigQuery
BigQuery is Google Cloud's fully managed, serverless data warehouse. "Serverless" means there are no warehouses to provision, size, or manage — you submit a query, BigQuery allocates compute automatically, and you pay for what was used (or for reserved slots you pre-purchased).
BigQuery stores data in Google's Colossus distributed storage, optimised for columnar access. Queries use Dremel (the distributed query engine) under the hood. Storage and compute are always separated — there is no warehouse provisioning analogous to Snowflake virtual warehouses.
Key BigQuery features:
- **On-demand pricing**: pay per TB of data queried (approximately $5/TB in most regions). No compute management.
- **Reserved slots**: pre-purchase query compute capacity (slots) for predictable high-volume workloads. More economical than on-demand at scale.
- **BigQuery ML (BQML)**: run ML model training and inference directly in SQL, without exporting data to a separate ML platform. Supports logistic regression, linear regression, k-means, time series, and integrates with Vertex AI for more complex models.
- **BigQuery Omni**: query data in AWS S3 and Azure ADLS Gen2 from BigQuery without moving data. Multi-cloud analytics without data movement.
- **Streaming inserts**: load data into BigQuery in real time via the streaming API. Native streaming ingestion without a separate pipeline.
- **Information Schema and INFORMATION_SCHEMA**: system views for metadata, usage, and query history.
- **GCP ecosystem integration**: native integration with Looker, Vertex AI, Dataflow (Apache Beam), Cloud Composer (Airflow), Data Studio/Looker Studio, Cloud Spanner, and Cloud Storage.
### Snowflake
Snowflake is an independent cloud data warehouse available on AWS, Azure, and GCP. Unlike BigQuery, Snowflake uses virtual warehouses — you provision and manage compute separately from storage.
Key Snowflake features:
- **Virtual warehouses**: managed compute clusters (X-Small to 4X-Large) that auto-suspend and auto-resume. You control which warehouse runs which workloads and at what cost. See Snowflake pricing guide for the full pricing breakdown.
- **Multi-cluster warehouses**: scale out (not just up) for concurrent workloads. Multiple clusters handle high concurrency without per-query queuing.
- **Zero-copy cloning**: clone databases, schemas, and tables without copying data. Development and test environments without storage duplication.
- **Time Travel**: query historical data states (up to 90 days on Enterprise). Undo accidental changes by selecting from a table as it existed at a past timestamp.
- **Data Sharing**: share live data with external organisations or Snowflake accounts without copying it — the receiver queries a live view of your data. The most mature data sharing capability in the warehouse market.
- **Snowpark**: run Python, Java, and Scala code directly in Snowflake using DataFrame APIs. ML model training (via Snowflake ML) and complex transformation without moving data out.
- **Cross-cloud deployment**: run Snowflake workloads on AWS, Azure, or GCP. Relevant for multi-cloud organisations or those requiring geographic deployment flexibility.
- **Extensive ecosystem**: dbt, Fivetran, Airbyte, Tableau, Power BI, Looker all have mature Snowflake integrations.
Head-to-head comparison
### Query performance
At most typical analytics scales (up to a few TB per query, standard aggregation patterns), BigQuery and Snowflake perform comparably. Both are columnar, distributed, and well-optimised for SQL analytics.
For very large queries (multi-TB scans), BigQuery's serverless model can be advantageous — Google allocates the compute needed automatically, without capacity planning. Snowflake virtual warehouses have a fixed size; very large queries may require a larger warehouse (at higher cost) or take longer on a smaller one.
For high-concurrency workloads (many simultaneous queries from BI tools), Snowflake's multi-cluster warehouse model scales concurrency effectively. BigQuery's serverless model handles concurrency without explicit configuration, but query prioritisation in high-contention environments is less controllable.
Advantage: effectively **even** at most enterprise scales. Snowflake provides more tuning levers; BigQuery is simpler to operate at scale.
### Pricing model
**BigQuery on-demand** ($5/TB queried): extremely simple, no capacity planning. The risk: expensive queries on wide tables without partition pruning or column selection can produce unexpected costs. Query costs are opaque until the query runs.
**BigQuery reserved slots**: pre-purchase a fixed compute capacity. More predictable costs for high-volume workloads. Less economical for low or variable workloads.
**Snowflake credits**: pay for compute when virtual warehouses are running. Auto-suspend prevents paying for idle time. More tuning levers (warehouse size, auto-suspend duration, resource monitors) for cost control. The risk: warehouses that do not auto-suspend waste money. For a full breakdown, see Snowflake pricing guide.
Advantage: **BigQuery** for simplicity; **Snowflake** for control. BigQuery on-demand is the simplest model for low-volume or unpredictable workloads. Snowflake gives more control for organisations that want to optimise costs actively.
### Google ecosystem integration
BigQuery's native integrations with Looker, Vertex AI, Dataflow, Cloud Composer, and Looker Studio are materially better than Snowflake's equivalents. For GCP-first organisations — particularly those using Looker as their BI tool, Vertex AI for ML, or Cloud Composer for orchestration — BigQuery removes integration friction that Snowflake would require additional configuration to achieve.
Advantage: **BigQuery** for GCP-first organisations.
### Data sharing
Snowflake's Data Sharing feature is the most mature data sharing capability in the warehouse market. Sharing live data with external organisations (Snowflake-to-Snowflake), monetising data products, and the Snowflake Data Marketplace (pre-built data products from third-party providers) are more developed in Snowflake than in any competitor including BigQuery.
Advantage: **Snowflake** for cross-organisation data sharing use cases.
### Multi-cloud
Snowflake runs on AWS, Azure, and GCP, with consistent behaviour across all three clouds. For organisations with multi-cloud data environments, Snowflake's cloud portability is valuable — the same Snowflake skills, tooling, and architecture work regardless of cloud.
BigQuery is GCP-only for compute (BigQuery Omni allows querying AWS/Azure data without moving it, but the compute runs on GCP). For organisations committed to GCP or willing to tolerate GCP-hosted compute, this is not a limitation. For organisations that need genuine multi-cloud data processing, Snowflake is more flexible.
Advantage: **Snowflake** for multi-cloud.
### ML capabilities
**BigQuery ML** allows training and inference directly in SQL, without moving data. For organisations where data scientists work in SQL or Python (via BigQuery DataFrames), BQML removes the data movement step between warehouse and ML platform. Integration with Vertex AI for more complex models is straightforward.
**Snowflake ML** (Snowpark ML) runs Python ML model training in Snowflake compute, using the Snowpark DataFrame API. The capability is growing but is less mature than Google's Vertex AI integration. For complex ML workloads, Snowflake users typically move data to a dedicated ML platform.
Advantage: **BigQuery** for ML/AI integration, particularly for GCP-native ML workflows.
### SQL dialect and compatibility
Both warehouses use ANSI-standard SQL with proprietary extensions. The dialects are similar for standard DML/DDL but differ in window functions, date functions, semi-structured data handling, and procedural SQL.
BigQuery uses StandardSQL (and legacy SQL for older queries). Snowflake uses a T-SQL-influenced dialect. Neither dialect perfectly matches SQL Server, PostgreSQL, or any other platform — some migration effort is required when moving SQL from on-premise systems.
Advantage: **even** for most SQL analytics workloads.
Decision framework
Choose BigQuery when:
- Your organisation is GCP-first and uses significant Google Cloud services
- Looker is your BI tool (native BigQuery integration)
- Vertex AI is your ML platform
- On-demand pricing model suits your usage pattern (low or variable workloads)
- Operational simplicity is a priority (no warehouse management)
- You need streaming ingestion without a separate pipeline tool (BigQuery streaming API)
Choose Snowflake when:
- Your organisation is multi-cloud (AWS, Azure, and GCP) or non-GCP primary
- Cross-organisation data sharing is a requirement
- You want fine-grained cost control with virtual warehouse management
- Your team prefers explicit compute management over serverless abstraction
- Snowflake's ecosystem integrations (Tableau, dbt, Fivetran) are already in use
- Zero-copy cloning and Time Travel are important for your development workflows
Both platforms handle well:
- Standard SQL analytics with dbt transformation
- Tableau and Power BI reporting
- Mid-market to large-enterprise data volumes
- Role-based access control and data masking
- HIPAA and SOC 2 compliance requirements
Frequently asked questions
We are on AWS. Should we use Snowflake or BigQuery?
For AWS-primary organisations, Snowflake on AWS is the more natural choice — it is hosted natively in your cloud, no cross-cloud egress, and the ecosystem is consistent. BigQuery can run in AWS via BigQuery Omni, but it adds complexity. Amazon Redshift is the third option worth evaluating for AWS-first organisations, though Snowflake has largely displaced Redshift in new enterprise builds.
Can we migrate from Snowflake to BigQuery (or vice versa)?
Yes, but it requires rewriting SQL (dialect differences), reconfiguring dbt models (some macros are warehouse-specific), migrating data (export/import or via a migration tool), and reconfiguring BI tool connections. The migration is feasible but is a multi-week project for large environments. Do not underestimate the SQL dialect differences in complex analytical queries.
Which is cheaper for our workload?
The honest answer: it depends entirely on your query patterns and concurrency. BigQuery on-demand is cheapest for infrequent queries on well-partitioned data. Snowflake auto-suspend is cheapest for predictable workloads with efficient idle management. For a definitive answer, run a proof of concept with representative queries on both platforms using your actual data and usage patterns.
For cloud data warehouse architecture design — selecting the right platform, designing the storage schema, setting up cost governance, and integrating with your existing BI stack — our cloud engineering practice does this work daily. Book a free 30-minute audit to discuss your specific requirements.
A former Microsoft data architect audits your data foundation, identifies your top priorities, and sends you a written plan. Free. No pitch.
Book a Call →