At a glance
| SQL support | GoogleSQL, with QUALIFY, ARRAY, STRUCT and JSON types |
| Storage and compute architecture | Managed columnar storage separate from slot-based compute; no clusters |
| Workload types | SQL analytics and BI, ELT, ML in SQL, data sharing, queries over lake tables |
| Integrations | Cloud Storage, Iceberg, Looker, Spark, S3 and Azure Blob via Omni |
| Deployment | Google Cloud regions and multi-regions; Omni in selected AWS and Azure regions |
| Pricing model | On demand per TiB processed, or slot-hours by edition; storage per GiB-month (logical or physical) |
BigQuery suits teams whose data already lives on Google Cloud and who want no compute objects to manage. On-demand billing makes occasional and exploratory use cheap to start, but costs follow bytes scanned, so partitioning, clustering and query habits drive the bill; editions and reservations give predictable spend at scale.
Overview
BigQuery is a fully managed warehouse in which columnar storage and a distributed compute layer are separate and scale independently. There are no clusters or warehouses to create: queries run on slots, virtual compute units that BigQuery allocates. Objects are addressed as project.dataset.table.
You pay for compute on demand, per TiB processed, or through capacity editions, where you buy slots in reservations with an optional baseline and autoscaling to a maximum. BigQuery ML trains and runs models in SQL, Apache Iceberg managed tables store data in your own Cloud Storage buckets, and BigQuery Omni runs queries over data in Amazon S3 and Azure Blob Storage in a few AWS and Azure regions.
Key features
- Serverless execution on slots, with no clusters or warehouses to size
- On-demand billing per TiB processed, with a maximum bytes billed setting to block costly queries
- Standard, Enterprise and Enterprise Plus editions with reservations and slot autoscaling in steps of 50
- Native JSON type plus typed ARRAY and STRUCT columns, expanded with UNNEST
- BigQuery ML for training and running models with SQL (not available in Standard edition)
- Apache Iceberg managed tables in your own Cloud Storage buckets
- BigQuery sharing (formerly Analytics Hub) with linked datasets and data clean rooms
Pros and cons
Pros
- Nothing to size or start before running a query
- Monthly free tier of 1 TiB of queries and 10 GiB of storage
- Per-query cost controls, plus reservations and commitments for predictable spend
- BigQuery Omni can query some S3 and Azure Blob data in place
Cons
- On-demand cost grows with bytes scanned, and LIMIT does not reduce it on non-clustered tables
- Autoscaled slots are billed as scaled, not as used
- Standard edition has no commitments, BigQuery ML or BI Engine, and a lower SLO (99.9%)
- Omni covers few regions and does not support DML or BigQuery ML
Pricing
BigQuery bills compute and storage separately. Compute is either on demand, charged per TiB of data a query processes, or capacity, charged per slot-hour through the Standard, Enterprise or Enterprise Plus edition with a one-minute minimum (per second with fluid scaling). Enterprise and Enterprise Plus offer one-year (20% off) and three-year (40% off) commitments; Standard has none. Storage is billed on logical or physical bytes, with a lower long-term rate for data not modified for 90 days. The Google Cloud free tier includes 1 TiB of queries and 10 GiB of storage per month, and new customers get USD 300 of credit for 90 days. Per-TiB and slot-hour rates vary by location and edition; Google's pricing page did not render for us, so we do not quote them. Use the Google Cloud pricing calculator.
Pricing checked on the vendor's official page on 7 November 2026. Prices change; confirm before buying.
Who it is best for
Teams on Google Cloud that want a warehouse with no compute to manage, with per-query billing to start and capacity editions as usage grows.
Who should look elsewhere
- Teams that need the same warehouse on AWS or Azure as a full deployment: see Snowflake vs BigQuery.
- Workloads built mainly on AWS: see BigQuery vs Redshift.
Verdict
BigQuery is the natural warehouse on Google Cloud, and its serverless model removes most capacity planning. The trade-off is that on-demand cost follows bytes scanned, so it rewards well-partitioned tables and careful queries; switch to editions when usage becomes steady.
Frequently asked questions
Is BigQuery free?
Partly. The Google Cloud free tier includes 1 TiB of query processing and 10 GiB of storage per month, and new customers get USD 300 of credit for 90 days. Usage beyond that is billed per TiB processed or by slot capacity.
What is the difference between on-demand and editions pricing?
On demand charges for the bytes each query processes and needs no setup. Editions (Standard, Enterprise, Enterprise Plus) charge per slot-hour for reserved and autoscaled capacity, and Enterprise and Enterprise Plus offer one- and three-year commitments at 20% and 40% discounts.
Can BigQuery query data in AWS or Azure?
Yes, through BigQuery Omni, in a small set of AWS regions and Azure East US 2. Google documents that Omni supports only Enterprise edition reservations or on-demand pricing and excludes DML, JavaScript UDFs and BigQuery ML.
Sources
- BigQuery documentation: Understand BigQuery editions
- BigQuery documentation: Understand slots
- BigQuery documentation: Estimate and control costs
- BigQuery documentation: Introduction to BigQuery Omni
- Google Cloud free tier
- BigQuery pricing
Checked November 2026.
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