Should I use Snowflake or BigQuery for a new warehouse?

It depends mainly on your cloud, team, and workload—but here’s the practical shortcut:

Quick rule of thumb

• Choose BigQuery if you’re already on Google Cloud, want the simplest ops, and prefer a serverless, highly managed warehouse.
• Choose Snowflake if you want stronger cross-cloud flexibility, very polished enterprise features, and more control over workload isolation and performance tuning.

BigQuery tends to be better when you want:

• Minimal infrastructure management
• Fast setup and easy scaling
• Strong integration with Google Cloud and the broader GCP ecosystem
• A usage model that works well for bursty analytics
• Good support for ad hoc SQL and BI workloads

Snowflake tends to be better when you want:

• Multi-cloud or cloud-agnostic strategy
• Easier separation of compute and storage with distinct virtual warehouses
• Fine-grained workload isolation for different teams/use cases
• Strong governance and sharing features
• A very mature enterprise analytics platform

Key differences that matter

1) Cloud fit

• BigQuery: best fit on GCP
• Snowflake: works well across AWS, Azure, and GCP

2) Operating model

• BigQuery: more “set it and forget it”
• Snowflake: still managed, but gives more knobs for workload management

3) Cost model

• BigQuery: often attractive for intermittent querying, but costs can surprise you with heavy scans if not managed well
• Snowflake: can be easier to reason about for dedicated workloads, but warehouse sizing and concurrency choices matter

4) Performance/workload isolation

• Snowflake: often stronger if many teams need independent compute
• BigQuery: very strong, but less about tuning compute per team

If you’re still undecided

Pick:

• BigQuery if you’re a GCP-first startup or team that wants speed and simplicity
• Snowflake if you’re an enterprise, multi-cloud, or expect lots of separate analytics workloads

If you want, I can also give you a decision matrix by use case like:

• startup vs enterprise
• BI dashboards vs data science
• batch ETL vs streaming
• expected monthly cost profile