Snowflake vs BigQuery: how they differ in compute, price and features

Both warehouses answer the same SQL at the same scale, and both keep storage apart from compute, so the data can grow without the query engine growing with it. They part ways on what you manage and what you are billed for. Snowflake gives you compute to size and charges for the time it runs. BigQuery gives you no compute to see and, on demand, charges for what each query reads.

What each one is

Snowflake keeps every table in its own storage, split into micro-partitions automatically, and runs queries on virtual warehouses. A warehouse is a cluster you name and size: X-Small uses 1 credit an hour, each size up doubles the credits, and query speed generally grows with the size. It runs only while there is work, suspending after the idle time you set and resuming on the next query, and it is billed by the second. A team can have a warehouse for loading, another for dashboards and another for data science, each sized for its job. Snowflake runs on AWS, Azure and Google Cloud.

BigQuery is Google Cloud's serverless warehouse. There is no cluster: each query gets slots, BigQuery's units of compute, from a shared pool. On demand you pay for the bytes a query reads, so how a table is laid out decides the bill: a query that filters on the partition column reads only those partitions. For steady loads you can buy slot capacity in an edition instead, and the bytes no longer matter.

Side by side

Which one fits

The same table in each

-- Rows kept together by day. Snowflake splits the table into micro-partitions itself.
create or replace table orders (
  order_id string, country string, amount number(10, 2), ordered_at timestamp_ntz
)
cluster by (to_date(ordered_at));

insert into orders values
  ('o-1', 'DE', 120.00, '2026-10-14 09:12:00'),
  ('o-2', 'FR', 80.50, '2026-10-14 11:40:00'),
  ('o-3', 'DE', 42.00, '2026-10-14 18:05:00'),
  ('o-4', 'IN', 15.25, '2026-10-15 07:30:00'),
  ('o-5', 'DE', 60.00, '2026-10-15 13:55:00');

-- Runs on the warehouse in use. The bill is the seconds it runs, not the bytes it reads.
select to_date(ordered_at) as order_date, country, sum(amount) as revenue
from orders
where to_date(ordered_at) between '2026-10-14' and '2026-10-15'
group by order_date, country
order by order_date, country;

-- One partition per day, and rows in each day sorted by country.
create or replace table shop.orders (
  order_id string, country string, amount numeric, ordered_at timestamp
)
partition by date(ordered_at)
cluster by country
options (require_partition_filter = true);

insert into shop.orders values
  ('o-1', 'DE', 120.00, timestamp '2026-10-14 09:12:00'),
  ('o-2', 'FR', 80.50, timestamp '2026-10-14 11:40:00'),
  ('o-3', 'DE', 42.00, timestamp '2026-10-14 18:05:00'),
  ('o-4', 'IN', 15.25, timestamp '2026-10-15 07:30:00'),
  ('o-5', 'DE', 60.00, timestamp '2026-10-15 13:55:00');

-- The date filter is required: it limits the scan, and the bill, to the days asked for.
select date(ordered_at) as order_date, country, sum(amount) as revenue
from shop.orders
where date(ordered_at) between '2026-10-14' and '2026-10-15'
group by order_date, country
order by order_date, country;

The SQL is nearly the same; the difference is in what each file says about cost. In BigQuery, the table's partition by and require_partition_filter exist because the bytes read are the bill: a query without a date filter is refused before it can read every day. In Snowflake, nothing in the file touches the bill. The same query costs the seconds the warehouse runs, so the levers are elsewhere: the warehouse's size, how fast it suspends, and whether a clustering key helps a large table prune micro-partitions.

Things that trip people up

• A BigQuery limit does not cut the bill. On demand, a query is charged for the data it reads, and on a table that is not clustered, select * ... limit 10 costs the same as without the limit. Select the columns you need, filter on the partition column, and check the bytes first with a dry run.
• A Snowflake warehouse that never suspends bills all day. Each query that arrives before the auto-suspend time keeps it running. A dashboard that refreshes every few minutes can keep a warehouse up around the clock.
• Snowflake's 60-second minimum. Each start is billed for at least a minute, so a warehouse that suspends after a few idle seconds and wakes for many small queries pays that minute again and again.
• BigQuery slots queue rather than fail. With capacity pricing, a busy hour does not raise the bill; queries wait for slots instead. Watch queue times, not just cost.
• Time travel windows differ. Snowflake's default is 1 day and BigQuery's 7. Do not count on Snowflake's history for longer than you have set.

Pricing your own workload

The prices behind these sums are on Tecyfy, read weekly from each maker: the Snowflake cost calculator prices warehouses by size, edition and region, idle time and storage, and the BigQuery cost calculator prices on-demand queries and slots in each edition. For Snowflake against Databricks on the same hours, see Snowflake vs Databricks. How a table is laid out matters to both, and partitioning and clusteringData Partitioning and ClusteringTwo ways to lay out a large table so a query reads only the files it needs: partitioning splits it by a column such as the date, clustering keeps similar values in the same files. explains how pruning works on each.