Pick the wrong data warehouse and you'll either overpay for capacity you'll never use, or hit a wall when you grow. The three most common defaults — Snowflake, BigQuery and Postgres — solve different problems. Here's when each wins.
Postgres: the right starting point
For 80% of companies under 100 people, Postgres is enough. It's your application's transactional database. You can run analytical queries against a read replica. It scales to billions of rows on reasonable hardware. You already know how to operate it.
Pick Postgres when:
- Your data is mostly your own application's tables, not external sources.
- Your dataset is <1TB or your query patterns are simple aggregations.
- You have engineering ownership and aren't paying anyone to maintain it.
You'll outgrow it when: you're doing repeated full-table scans that take minutes, your transactional workload is suffering, or you've started joining across many sources and need a real columnar engine.
BigQuery: serverless, pay-per-query
BigQuery is Google's columnar warehouse. Serverless model: no clusters to size, pay for the bytes scanned per query. Excellent if you're already in Google Cloud and your query patterns are bursty.
Pick BigQuery when:
- You're a Google Cloud shop or already use other Google services (GA4, Google Ads, Sheets).
- Your query pattern is bursty — periods of activity separated by idle time. Serverless billing helps here.
- You want to ramp up without making a cluster-sizing decision.
The catch: if your query patterns become continuous (think dashboards refreshing every minute), the pay-per-byte model gets expensive fast. Reserved-capacity pricing solves this but undoes the "no decisions" benefit.
Snowflake: the enterprise default
Snowflake is the data warehouse everyone benchmarks against. Compute-and-storage-separated architecture, multi-cloud, mature ecosystem of tooling and partners. The default pick for an enterprise that wants a data team to use it as their daily driver.
Pick Snowflake when:
- You have a data team or are about to hire one.
- You're loading many sources and need a sustained ELT workflow with tools like dbt, Fivetran, etc.
- You're committing to "warehouse-first" data architecture as a strategy.
- You have multi-cloud needs (Snowflake runs on AWS, GCP and Azure).
The catch: Snowflake credits add up. Plan to spend 5-figures/month at any meaningful scale. The ecosystem will gladly help you spend that.
How this affects your BI tool choice
Different BI tools assume different warehouse shapes. Tableau and Power BI are warehouse-agnostic. Looker prefers BigQuery. Most "modern BI" tools assume Snowflake. clariBI is the outlier — designed to query operational tools (Stripe, HubSpot, Linear) directly via MCP without a warehouse step, and to add Postgres / BigQuery / Snowflake when your stack adds them.
For most companies under 50 people, the right answer is: stay on Postgres, point a BI tool at it, and add a warehouse only when the pain of not having one is louder than the pain of running one.