Snowflake vs BigQuery

Quick verdict

Snowflake is a managed data platform that runs only on public cloud infrastructure; Snowflake's documentation states it cannot be installed on premises or on private clouds. Its architecture has three layers: database storage, where data is reorganised into a compressed columnar format divided into micro-partitions; compute, provided by virtual warehouses (clusters you create, size, suspend and resume); and cloud services, which handle metadata, security and query optimisation. Each Snowflake account is created on one cloud (AWS, Microsoft Azure or Google Cloud) and one region, and you can have accounts on different clouds.

BigQuery is Google Cloud's serverless data warehouse. Storage is columnar and separate from compute, and there are no clusters to create: queries run on slots, which Google describes as virtual compute units allocated by the BigQuery scheduler. You pay for compute either on demand (per TiB processed) or through capacity pricing with one of three editions, Standard, Enterprise and Enterprise Plus, where you buy slots in reservations. BigQuery runs in Google Cloud regions; BigQuery Omni extends querying to data in Amazon S3 and Azure Blob Storage in a small set of AWS and Azure regions.

Side by side

Key differences

Architecture: warehouses you size against slots you rent

In Snowflake, every query and DML statement needs a running virtual warehouse. You choose its size, and Snowflake documents that credit consumption doubles with each size step, from 1 credit per hour for X-Small to 256 for 5X-Large. Warehouses auto-suspend after a period of inactivity and auto-resume when a statement arrives, both enabled by default. Different teams usually get different warehouses, so a heavy data load does not slow a dashboard. Snowflake also offers Snowpark-optimised warehouses and a newer Gen2 variant of the standard warehouse.

In BigQuery there is nothing to size before you run a query. On demand, a project draws slots from a shared pool up to a concurrent slot limit. With capacity pricing you create reservations, assign projects to them, and optionally set baseline slots plus a maximum that autoscaling can reach. Google documents that idle slots from one reservation can be borrowed by others in the same administration project, but not across editions. The trade-off is control: Snowflake gives you an explicit compute object per workload, while BigQuery hides compute until you choose to manage it through reservations.

Billing units and what drives the bill

A Snowflake bill is mostly credits (warehouse size x running time, billed per second after the first minute of each start) plus storage on the average compressed volume per month. The price of a credit depends on edition, cloud and region. Because the meter runs while a warehouse is up, auto-suspend settings and warehouse size matter more than how much data a query touches.

A BigQuery bill on the on-demand model is driven by bytes processed. Google's cost guidance notes that adding LIMIT to a query on a non-clustered table does not reduce the bytes read, and recommends partitioning, clustering and selecting only needed columns. A maximum bytes billed setting makes a query fail without charge if its estimate exceeds the limit. On capacity pricing you pay for slot-hours instead, so cost follows reserved and autoscaled slots rather than data scanned. Storage is billed separately, on logical (uncompressed) or physical (compressed) bytes, with a lower long-term rate for tables or partitions not modified for 90 days.

Concurrency and scaling controls

Snowflake scales up by resizing a warehouse and out with multi-cluster warehouses, an Enterprise edition feature. In auto-scale mode you set minimum and maximum cluster counts; the Standard policy starts clusters as soon as queries queue, while Economy only starts one if it is expected to stay busy for at least six minutes. Each running cluster consumes credits at the warehouse size rate.

BigQuery's scheduler shares slots fairly between projects in a reservation and then between jobs in a project. Autoscaling reservations add slots in multiples of 50 up to the maximum you set, and Google documents that you are charged for the slots scaled, not the slots actually used. Standard edition offers autoscaling only; Enterprise and Enterprise Plus add baseline slots and one- or three-year commitments.

Semi-structured data

Snowflake stores JSON and similar data in VARIANT columns (with OBJECT and ARRAY), up to 128 MB of uncompressed data per value, navigated with the colon path operator and expanded into rows with FLATTEN. BigQuery has a native JSON type with field access, JSON_VALUE and LAX_ conversion functions; Google lists a 500-level nesting limit and notes that JSON columns cannot be used for partitioning or clustering. BigQuery also has typed ARRAY and STRUCT columns, which many schemas use instead of JSON.

Snowflake (expand an array inside a VARIANT column):

SELECT e.payload:customer.id::STRING AS customer_id,
       i.value:sku::STRING          AS sku
FROM events e,
     LATERAL FLATTEN(INPUT => e.payload:items) i;

BigQuery GoogleSQL (the same against a JSON column):

SELECT JSON_VALUE(e.payload, '$.customer.id') AS customer_id,
       JSON_VALUE(item, '$.sku')             AS sku
FROM mydataset.events AS e,
     UNNEST(JSON_QUERY_ARRAY(e.payload, '$.items')) AS item;

SQL dialect differences

Both dialects support QUALIFY to filter on window functions without a subquery, so "latest row per group" reads the same way:

-- Snowflake
SELECT customer_id, order_id, order_ts
FROM orders
QUALIFY ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_ts DESC) = 1;

-- BigQuery GoogleSQL
SELECT customer_id, order_id, order_ts
FROM mydataset.orders
WHERE order_ts IS NOT NULL
QUALIFY ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_ts DESC) = 1;

Date functions differ in argument order, which is a common source of bugs when porting SQL. Snowflake's DATEDIFF takes the unit first and subtracts the second date from the third; BigQuery's DATE_DIFF takes the later date first and the unit last. DATE_TRUNC is also reversed.

-- Snowflake
SELECT DATEDIFF(day, start_date, end_date) AS days_open,
       DATE_TRUNC('month', start_date)     AS start_month
FROM tickets;

-- BigQuery GoogleSQL
SELECT DATE_DIFF(end_date, start_date, DAY) AS days_open,
       DATE_TRUNC(start_date, MONTH)       AS start_month
FROM mydataset.tickets;

Object naming differs too: Snowflake uses database.schema.table, while BigQuery uses project.dataset.table, with backticks when a project ID contains hyphens.

Data sharing and multi-cloud reach

Snowflake Secure Data Sharing copies no data: the consumer queries the provider's objects read-only and pays only for its own compute. Direct shares work within one region; sharing across regions or clouds needs a listing with cross-cloud auto-fulfilment or replication. Snowflake Marketplace carries free and paid listings and is available to accounts on all three clouds, and reader accounts let a provider share with organisations that have no Snowflake account.

BigQuery sharing (formerly Analytics Hub) uses exchanges and listings; subscribers get read-only linked datasets without copies, commercial listings go through Google Cloud Marketplace, and data clean rooms are supported. For data held outside Google Cloud, BigQuery Omni runs the BigQuery engine in AWS regions (including US East N. Virginia, US West Oregon, Ireland and Frankfurt) and Azure East US 2, over BigLake tables. Google documents limits: Omni is supported only with Enterprise edition reservations or on demand, and it does not support DML, JavaScript UDFs or BigQuery ML statements. Snowflake, by contrast, lets you create a full account on another cloud.

Pricing and licensing

Snowflake. According to Snowflake's pricing page in October 2026, you pay for compute in credits and for storage monthly on the average compressed volume. The credit price depends on the edition (Standard, Enterprise, Business Critical or Virtual Private Snowflake), cloud and region, and capacity can be bought on demand or pre-paid. Snowflake's documentation lists warehouse consumption from 1 credit per hour (X-Small) to 256 credits per hour (5X-Large), billed per second with a 60-second minimum each time a warehouse starts; a multi-cluster warehouse consumes credits for each running cluster. Per-credit prices are shown through the pricing page and Snowflake's Service Consumption Table and calculator, which we could not read reliably, so none are quoted. The trial lasts 30 days or until its free usage balance is spent, whichever comes first.

BigQuery. Compute is either on demand, charged per TiB processed, or capacity, charged per slot-hour for the Standard, Enterprise or Enterprise Plus edition, with a one-minute minimum (or per-second billing with fluid scaling). Google's editions documentation lists a 20% discount for a one-year and 40% for a three-year commitment on Enterprise and Enterprise Plus; Standard and on demand have no commitments. Storage is billed on logical or physical bytes, with active and long-term rates. Google Cloud's free tier includes 1 TiB of query processing and 10 GiB of storage per month, and new customers receive USD 300 of credit for 90 days. We could not read Google's BigQuery pricing page in full, so no per-TiB or slot-hour prices are quoted; use the Google Cloud pricing calculator.

The two bills respond to different behaviour: Snowflake to how long warehouses run and at what size, BigQuery on demand to how many bytes queries read. Model your own workload in both calculators rather than comparing headline unit prices.

Pricing checked on the vendors' official pages on 7 October 2026. Prices change; confirm before buying.

Where each one leads

Limitations

When to choose each

When neither is right

• Your data lives on AWS and you want a native service: compare Snowflake vs Redshift and BigQuery vs Redshift.
• Your workload is mainly Spark, data engineering or machine learning on open table formats: see Snowflake vs Databricks and Databricks vs BigQuery.
• Your data fits comfortably in one database: you may not need a warehouse at all; see Data warehouse vs database and BigQuery vs PostgreSQL.
• You want to look beyond both: see Snowflake alternatives and BigQuery alternatives.

Final recommendation

Frequently asked questions

Sources

• Snowflake pricing options
• Snowflake documentation: Key concepts and architecture
• Snowflake documentation: Overview of warehouses
• Snowflake documentation: Multi-cluster warehouses
• Snowflake documentation: Supported cloud platforms
• Snowflake documentation: Semi-structured data types
• Snowflake documentation: About Secure Data Sharing
• Snowflake documentation: Trial accounts
• BigQuery documentation: Understand BigQuery editions
• BigQuery documentation: Understand slots
• BigQuery documentation: Slots autoscaling
• BigQuery documentation: Estimate and control costs
• BigQuery documentation: Working with JSON data
• BigQuery documentation: Introduction to BigQuery Omni
• BigQuery documentation: BigQuery sharing (formerly Analytics Hub)
• Google Cloud free tier
• BigQuery pricing

Checked October 2026.

How we research comparisons: our editorial method.