BigQuery vs Snowflake
Pricing, performance, and dbt fit for the two warehouses analytics engineers use most.
BigQuery is Google Cloud's serverless warehouse: you never size compute, and on-demand queries bill by the bytes they read (or you buy slot capacity instead). Snowflake runs on AWS, Azure, or Google Cloud and bills credits for each second its virtual warehouses run. Pick BigQuery for GCP-native, bursty, low-admin work; pick Snowflake for multi-cloud teams, steady pipelines, and explicit control over compute.
- Option A
- BigQuery
- Option B
- Snowflake
- Compared on
- 11 dimensions
- Goes deeper in
- BigQuery for Analytics Engineers
BigQuery and Snowflake both separate storage from compute, both scale to very large tables, and both speak SQL close enough that a dbt project moves between them with targeted changes rather than a rewrite. The feature checklists look alike. The operating models do not.
The real difference is what you pay for. BigQuery's default model charges for the data a query reads, so cost follows table design and query shape. Snowflake charges for the time a warehouse runs, so cost follows warehouse size, how long it stays on, and how many you run. Most of the practical advice for each platform falls out of that one distinction.
This page covers the pricing mechanics, performance and concurrency, and what changes in a dbt project, then gives decision rules. It sticks to how each platform works rather than list prices, which change; check the vendor pricing pages linked at the end for current rates.
| Aspect | BigQuery | Snowflake |
|---|---|---|
| Cloud | Google Cloud only | AWS, Azure, or Google Cloud |
| Compute model | Serverless; no clusters to size | Virtual warehouses you size, start, and suspend |
| Pricing unit | Bytes read per query (on-demand) or slot-hours (editions) | Credits per second a warehouse runs, 60-second minimum per start |
| Idle cost | None on-demand; a reserved slot baseline bills whether used or not | None once a warehouse auto-suspends |
| Concurrency | Queries share slots; autoscaling reservations add capacity | Multi-cluster warehouses add clusters (Enterprise Edition and up) |
| Main cost levers | Partitioning, clustering, column selection, bytes-billed limits | Warehouse size, auto-suspend, resource monitors, separate warehouses |
| Workload isolation | Reservations assigned to projects, folders, or the organization | A separate warehouse per team or job |
| Semi-structured data | STRUCT, ARRAY, and JSON types; UNNEST | VARIANT, OBJECT, and ARRAY types; LATERAL FLATTEN |
| dbt adapter specifics | partition_by, cluster_by, insert_overwrite | Transient tables by default, per-model warehouse, dynamic tables |
| Admin overhead | Very low | Low to moderate (warehouse sizing and policies) |
| Best for | GCP-native, bursty, or ad hoc workloads | Multi-cloud teams and steady, heavy pipelines |
How BigQuery and Snowflake pricing works
BigQuery on-demand pricing charges per TiB of data each query reads, after a small monthly free allowance. Run time does not matter; the columns and partitions a query touches do. A SELECT * across a wide, unpartitioned table is the classic expensive mistake. Selecting only the columns you need and filtering on the partition column is the fix. Adding LIMIT usually does not reduce the bytes billed.
BigQuery's capacity model (editions) replaces per-byte billing with slots, BigQuery's unit of compute, billed per slot-hour. Reservations can autoscale between a baseline and a maximum. Teams usually move to capacity pricing once on-demand spend is large and steady, because the bill becomes predictable and queries compete for slots instead of costing per byte.
Snowflake bills credits for every second a virtual warehouse runs, with a one-minute minimum each time it starts or resumes. Each size step roughly doubles both compute and credits per hour, so an X-Small running for an hour and a Small running for thirty minutes cost about the same. Storage is billed separately, as are serverless features, and cloud services usage is billed only when it exceeds 10% of that day's warehouse usage.
The practical upshot: on BigQuery you control cost through how much data queries read. On Snowflake you control cost through how big your warehouses are and how long they stay on.
Performance and concurrency
Neither platform is faster across the board. Speed depends on data layout, query shape, and how much compute is available at that moment. Published benchmarks are usually run or sponsored by a vendor, so test with your own slowest queries before deciding.
On BigQuery, the main performance levers are partition pruning and clustering, the same levers that control on-demand cost. Under capacity pricing, many concurrent queries share the reservation's slots, and autoscaling adds slots up to the maximum you set.
On Snowflake, a larger warehouse speeds up big individual queries, while a multi-cluster warehouse adds clusters when many users query at once. Micro-partitions and optional clustering keys handle pruning. Both platforms return cached results for repeated identical queries without charging compute.
dbt on BigQuery vs dbt on Snowflake
Sources, refs, tests, documentation, and project structure work the same way on both. The differences show up in model configs and in dialect-specific SQL:
- On BigQuery, set partition_by and cluster_by on large models, and add require_partition_filter where you want downstream queries to fail rather than scan the whole table.
- The insert_overwrite incremental strategy replaces whole partitions and is usually cheaper than merge on large BigQuery tables.
- On Snowflake, dbt creates transient tables by default. They skip Fail-safe storage, which lowers cost; set transient: false on tables that need Fail-safe recovery.
- dbt-snowflake accepts a snowflake_warehouse config per model, so one heavy model can run on a larger warehouse without resizing the default.
- Snowflake dynamic tables are available as a dbt materialization with a target_lag, for models Snowflake should keep refreshed on its own schedule.
- Date, string, and semi-structured functions differ, for example UNNEST on BigQuery versus LATERAL FLATTEN on Snowflake. dbt's cross-database macros cover many common cases if you need a project to run on both.
Lean BigQuery when
- You're already on Google Cloud or working with GA4, Google Ads, or Firebase exports.
- You want no compute administration: no warehouses to size, suspend, or monitor.
- Workloads are bursty or ad hoc, so paying per query beats paying for running compute.
- Your team will partition and cluster large tables and select columns deliberately.
Lean Snowflake when
- You need to run on AWS or Azure, or keep the option of changing clouds.
- Pipelines are steady and heavy, so right-sized warehouses give predictable spend.
- You want to isolate workloads with a separate warehouse per team or job.
- Sharing data with other Snowflake accounts or partners is central to the use case.
Most teams don't pick between these on features. They pick on which cloud they already run and on whether they'd rather control cost through query and table design (BigQuery) or through compute sizing and uptime (Snowflake).
For learning, the choice barely matters: SQL, dimensional modeling, and dbt patterns transfer directly. The capstone uses BigQuery because it is the fastest to stand up for free, and everything in it applies on Snowflake.
- Is BigQuery or Snowflake cheaper?
- It depends on the workload. BigQuery on-demand tends to be cheaper for bursty, ad hoc querying on well-partitioned tables, because you pay nothing between queries. Snowflake tends to be cheaper for steady pipelines you can fit to a right-sized warehouse that suspends quickly. Both have capacity or commitment options, so estimate against your real query history.
- Is BigQuery faster than Snowflake?
- Not consistently. Each can be faster depending on data layout, query shape, and available compute. Vendor benchmarks are not a reliable guide; run your own slowest queries on both.
- Should I learn BigQuery or Snowflake first?
- Either. The SQL, modeling, and dbt skills are the same on both. Pick whichever your target employers use, or start with BigQuery because its sandbox needs no credit card.
- Do dbt models work on both?
- Yes. dbt has maintained adapters for both. Expect to adjust warehouse-specific configs (partitioning on BigQuery, warehouses and transient tables on Snowflake) and some dialect functions, while project structure, tests, and modeling patterns stay the same.
Go deeper in the BigQuery for Analytics Engineers hub.