Google BigQuery and Snowflake are two of the most popular cloud data warehouses. Both run SQL on huge datasets without servers to manage, both store data cheaply, and both added a lot of AI in 2026. The real differences are in how you pay, which cloud you live in and what kind of AI help you want. Most comparisons online are a year or two old; this one uses prices and features as of November 2026.
The two models in one picture
BigQuery
Serverless by default- Compute
- slots, shared and allocated automatically
- Pricing
- per TiB scanned, or slot-hours with editions
- Cloud
- Google Cloud (Omni for AWS and Azure data)
- AI
- Gemini in SQL functions, data agents
- Nothing to size or switch on; queries just run
- Generous free tier for small teams
- A careless SELECT * on a big table costs real money
Best for Google Cloud shops and spiky, ad hoc analysis.
Snowflake
Warehouses you control- Compute
- virtual warehouses, sized XS to 6XL
- Pricing
- credits per second while a warehouse runs
- Cloud
- AWS, Azure, Google Cloud, and soon Oracle
- AI
- Cortex functions, CoCo and CoWork agents
- Very predictable when warehouses auto-suspend
- Strong governance and data sharing between companies
- An idle warehouse left running keeps billing
Best for multi-cloud companies and teams that want agents with governance.
Pricing, side by side
| BigQuery | Snowflake | |
|---|---|---|
| Query compute | On-demand: $6.25 per TiB scanned, first 1 TiB a month free. Or editions: $0.04 (Standard), $0.06 (Enterprise), $0.10 (Enterprise Plus) per slot-hour | Credits: $2 (Standard), $3 (Enterprise), $4 (Business Critical) in the US; $2.60 / $3.90 / $5.20 in Dublin and Frankfurt. An X-Small warehouse uses 1 credit an hour, and each size doubles |
| Billing unit | Bytes scanned (10 MB minimum per query) or slot-seconds | Per second, with a 60-second minimum each time a warehouse starts |
| Storage | About $0.023 per GiB a month active, about $0.016 long-term, first 10 GiB free | $23 per TB a month on demand in the US, less with capacity contracts |
| Free tier | 1 TiB queries and 10 GiB storage every month | 30-day trial only |
| Hidden costs to watch | Full-table scans, streaming inserts, BI Engine | Warehouses left running, serverless features (up to 2x multipliers), Gen2 warehouses at 1.25 to 1.35x |
A worked example
A small analytics team runs about 300 queries a working day on a 2 TB dataset. Each query scans about 2 GiB, so 22 working days scan roughly 13 TiB. These are illustrative list prices, before discounts:
Assumptions: 13 TiB scanned a month; Snowflake Enterprise X-Small warehouse active 6 hours a day for 22 working days, auto-suspend on. Storage excluded. Real costs depend heavily on query design and warehouse settings.
For this light, bursty workload, BigQuery on-demand wins easily: (13 − 1 free TiB) × $6.25 = $75. The picture flips for heavy, constant workloads, where scanning hundreds of TiB a day makes on-demand expensive and a reserved BigQuery edition or a well-tuned Snowflake warehouse is cheaper and more predictable. Always test with a week of your own query history.
AI features in November 2026
- BigQuery: AI in SQL
- Functions such as AI.AGG (summarise a group of rows with an instruction), AI.CHUNK_DOCUMENT, AI.TRANSLATE and AI.FORECAST with TimesFM 3.0, powered by Gemini models including Gemini 4 Argon.
- BigQuery: data agents
- Conversational analytics agents that answer questions in plain language, can be published to Gemini Enterprise and log every conversation.
- Snowflake: Cortex
- AI functions in SQL, Cortex Search, and Cortex Training to post-train open models on your data (preview).
- Snowflake: agents
- CoCo for data engineers and CoWork for business users, now running in the cloud and on schedules, with Business Ontology (private preview) to keep definitions consistent.
- Governance
- Snowflake's Cortex AI Gateway adds spend controls, tracing and abuse detection for agents; BigQuery relies on Google Cloud IAM and Dataplex for governance.
The short version: BigQuery makes AI feel like more SQL functions, which suits analysts. Snowflake is building a full agent platform with a governed business layer, which suits companies that want AI working across teams. See Snowflake Expedition 2026 and the BigQuery October update for the details.
Other differences that matter
| BigQuery | Snowflake | |
|---|---|---|
| Open table formats | Iceberg through BigLake and the Lakehouse runtime catalog | Managed Iceberg tables, Iceberg v3 upgrades GA |
| Real-time | Streaming inserts and Storage Write API | Snowpipe Streaming up to 20 GB/s per table; Interactive Analytics GA |
| Spark | Serverless Spark through Google Cloud | Snowpark Connect runs Spark code on Snowflake |
| BI | Looker, Looker Studio, Connected Sheets | Works with any BI tool; Streamlit apps built in |
| SQL dialect | GoogleSQL, now with reusable column aliases (opt-in) | Snowflake SQL, close to ANSI with extras such as QUALIFY |
How to choose
- 1
Start from your cloud
If almost everything runs on Google Cloud, BigQuery is the natural fit. If you use AWS or Azure, or several clouds, Snowflake avoids moving data between clouds.
- 2
Look at your query pattern
Light and spiky favours BigQuery on-demand. Heavy and steady favours reserved capacity on either platform.
- 3
Decide what AI help you want
SQL functions for analysts point to BigQuery; governed agents for the whole business point to Snowflake.
- 4
Keep data portable
Store large tables in Apache Iceberg so another engine can read them later.
- 5
Run a two-week trial
Load a real dataset, replay a week of queries, and compare cost and speed. Results beat any comparison article, including this one.
Check your understanding
1/3How does BigQuery on-demand charge for queries?
It is $6.25 per TiB scanned, with the first 1 TiB each month free.
2/3What keeps billing on Snowflake if you forget about it?
Set auto-suspend so warehouses stop when idle; billing is per second with a 60-second minimum.
3/3Which helps avoid lock-in on both platforms?
Both support Iceberg, so other engines can read the same tables.
You got 0 out of 3.
More comparisons: Snowflake vs Databricks for AI agents, Microsoft Fabric vs Databricks, Power BI vs Tableau and how to choose a database.
Sources: BigQuery pricing as recorded by StackScored (April 2026 snapshot) and Google's BigQuery release notes (October 2026); Snowflake credit and storage prices from Snowflake's consumption table as summarised by AgentSQL (September 2026); Snowflake Expedition 2026 announcements (3 November 2026). Prices are list prices before discounts and vary by region; check both vendors' pricing pages before budgeting.
Alessandro Ecclesie AgazziWritten by Alessandro Ecclesie Agazzi, analytics engineer in London. Updated 3 November 2026. About me