Cloud data warehouses · Google LLC (Google Cloud)

Google BigQuery

Serverless cloud data warehouse billed by data scanned or by slot capacity, with no infrastructure to provision.

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BigQuery is Google Cloud's serverless, columnar data warehouse. There are no clusters or instances to size: Google allocates compute automatically per query, and customers choose between on-demand pricing (pay per byte scanned) or capacity pricing (reserve slots, i.e. virtual CPUs, across Standard, Enterprise, or Enterprise Plus editions). It stores data natively in a proprietary columnar format, supports federated queries over Cloud Storage and other sources via BigQuery Omni, and includes BigQuery ML for training models with SQL and BI Engine for sub-second dashboard queries. It integrates tightly with the rest of Google Cloud (Looker, Vertex AI, Dataflow) and is delivered exclusively as a managed cloud service.

At a glance

Vendor Google LLC (Google Cloud)
Pricing model Usage-based
Free tier Yes
Deployment Cloud
Open source No
Best for Teams already on Google Cloud who want a serverless warehouse with no cluster management.

Pricing

On-demand queries are billed per tebibyte scanned with 1 TiB free per month; capacity pricing bills per slot-hour by edition (Standard, Enterprise, Enterprise Plus); storage is billed separately per GiB, discounted after 90 days of inactivity.

Plan Price Notes
On-demand queries $6.25 per TiB Iowa (us-central1); first 1 TiB per month free
Standard edition slots $0.04 per slot-hour Iowa (us-central1); pay-as-you-go, billed per second with 1-minute minimum
Enterprise edition slots $0.06 per slot-hour Iowa (us-central1); pay-as-you-go; discounted with 1- or 3-year commitments
Active logical storage $0.000031507 per GiB-hour Iowa (us-central1); first 10 GiB per month free; drops ~50% after 90 days untouched

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Serverless architecture with automatic compute allocation
  • On-demand (per-TiB) or capacity (per-slot) pricing models
  • BigQuery ML for in-warehouse model training via SQL
  • BI Engine in-memory acceleration for dashboards
  • BigQuery Omni for federated queries over other clouds
  • Native streaming ingestion via the Storage Write API
  • Materialized views and automatic query result caching
  • Fine-grained IAM, column-level security, and row-level access policies

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Snowflake Cloud-native data warehouse that separates storage and compute and bills usage in per-second credits. Amazon Redshift AWS's managed cloud data warehouse, available as provisioned clusters or an auto-scaling serverless option. Databricks Managed lakehouse platform, built by Apache Spark's creators, combining Spark-based compute with governance and ML tooling.

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Terms to know

Data warehouse A centralized, structured database optimized for fast historical reporting rather than day-to-day transactions. OLAP (online analytical processing) A category of technology optimized for fast, multi-dimensional aggregation and slicing of data for analysis. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data modeling The practice of organizing data into entities, relationships and schemas that make it accurate and efficient to query.

Related guides

How to build a modern data stack Ingestion, warehouse, transformation, BI and activation — what a data stack needs at each company stage, and what to defer. How to choose a cloud data warehouse Start from your cloud, your pricing tolerance and your workload shape — general BI, high-concurrency apps, or enterprise mixed workloads — not the feature list. How to reduce data warehouse costs Where usage-based warehouse spend actually goes, and the levers — query, storage, scheduling — that reliably bring it down.