We are a BigQuery shop, so read this with that in mind. But we have migrated teams in both directions and the honest version of this comparison is more useful to you — and to us — than a sales page. This is written for mid-market data teams: a few terabytes to a few hundred, a handful of engineers, and a budget someone actually reviews.
Pricing philosophy
Snowflake bills compute by the virtual warehouse: you pick a size (X-Small through 6X-Large), it charges credits per second while running, and auto-suspends when idle. Cost is a function of time a warehouse is on, which makes it intuitive and makes it easy to leave a warehouse running.
BigQuery offers two models: on-demand (per TiB scanned — cost is a function of data touched, regardless of time) and Editions (per slot-hour, autoscaled in 50-slot steps). On-demand punishes unfiltered scans and rewards good partitioning; Editions behave more like Snowflake's warehouses but with finer-grained autoscaling and no manual sizing.
What this means in practice: teams with disciplined data modelling and bursty analyst usage tend to pay less on BigQuery on-demand, because idle time costs nothing and good partitioning makes scans cheap. Teams with steady, predictable pipeline load find the two platforms closer than the marketing suggests, and the decision comes down to discounts and the rest of the stack. Teams with undisciplined SELECT * habits get surprised on BigQuery on-demand and should use Editions with per-user byte quotas — or fix the habit.
Operations
Snowflake's warehouse model gives you a knob for isolation: dedicated warehouses per team mean one team's bad query cannot slow another's. BigQuery achieves the same with reservations and assignments per project, which is less visible but also means nothing to size by hand. BigQuery has no clusters to suspend or resume and no storage tiers to manage; Snowflake has more knobs and more things that can be left on.
Both are fully managed; neither needs a DBA in the traditional sense. BigQuery's INFORMATION_SCHEMA views are more extensive out of the box for cost attribution; Snowflake's ACCOUNT_USAGE schema is comparable once you know it.
AI features
This is where the platforms have diverged most since 2024.
BigQuery has pulled AI into SQL: AI.GENERATE and ML.GENERATE_TEXT over Gemini, ML.GENERATE_EMBEDDING, vector indexes with VECTOR_SEARCH, BigQuery ML for classical models and TimesFM forecasting, and — since April 2026 — a GA Data Engineering Agent that generates Dataform pipelines. Gemini is in the editor for SQL generation and explanation.
Snowflake's answer is Cortex: COMPLETE, EMBED_TEXT, Cortex Search for hybrid retrieval, Cortex Analyst for natural-language questions over semantic models, and an expanding set of agents. It supports a broader menu of third-party models (Anthropic, Meta, Mistral, OpenAI via Azure) alongside its own, where BigQuery's path is Vertex AI and Gemini with the option of open models through Vertex Model Garden.
Rough parity on capability; different centres of gravity. If your organisation is standardised on Google's models, BigQuery's integration is tighter and the governance story — row-level security applies to retrieval — is cleaner. If you want model choice as a first-class feature, Snowflake currently offers more.
Open formats and the lakehouse
Both now support Apache Iceberg as a managed table format with an Iceberg REST catalog: Snowflake via Iceberg Tables and Open Catalog (Apache Polaris), BigQuery via managed Iceberg tables and the BigLake metastore. Both let Spark and Trino read the same data without copies. Snowflake was earlier to make Iceberg a headline feature; BigQuery's implementation is now at parity for the cases mid-market teams encounter, and its integration with Google Cloud Storage and Dataproc is naturally deeper. On GCP, choose BigQuery; on AWS or Azure, Snowflake's multi-cloud story is simply more relevant.
Ecosystem
Snowflake's Marketplace and data-sharing features are more mature for buying and selling datasets across company boundaries. BigQuery's Analytics Hub covers the same ground with a smaller catalogue, offset by direct integration with Google Ads, GA4, Google Marketing Platform, and the rest of Google Cloud. If marketing analytics is your centre of gravity, BigQuery's native exports from Google properties alone can decide the question.
A decision guide for mid-market teams
Choose BigQuery when:
- You are on Google Cloud or rely on GA4/Google Ads data
- Analyst usage is bursty and you are willing to model data well
- You want AI features inside SQL with governance inherited automatically
- You want minimal operational surface — nothing to size, suspend, or resume
Choose Snowflake when:
- You are on AWS or Azure, or must span clouds
- Data sharing with external partners is a core workflow
- You want a choice of LLM vendors as a platform feature
- Your team already knows it and the migration cost outweighs any saving
Either platform will serve a mid-market team well for the next five years. The expensive mistake is not choosing the "wrong" one; it is choosing one and then running it with the other's habits — warehouse-style always-on thinking on BigQuery on-demand, or scan-anything thinking on a Snowflake warehouse you forgot to suspend.
If you are weighing a migration in either direction and want numbers rather than opinions, contact us. We will tell you if BigQuery is not the right answer.