Google BigQuery
Fully managed, serverless data warehouse and analytics platform on Google Cloud with AI/ML features (BigQuery ML, vector search, Gemini).
Regional availability
- US (multi-region)
- EU (multi-region)
- us-central1 (Iowa)
- us-east1 (South Carolina)
- europe-west1 (Belgium)
- europe-west3 (Frankfurt)
- asia-northeast1 (Tokyo)
- asia-south1 (Mumbai)
- australia-southeast1 (Sydney)
Description
Google BigQuery is a fully managed, serverless data warehouse and analytics platform on Google Cloud. It originated from Google’s internal Dremel technology; it was announced in 2010 and became generally available in November 2011. It lets users analyze large datasets with standard SQL without managing infrastructure.
The platform integrates AI/ML capabilities: BigQuery ML lets you create, train, and run machine learning models directly in SQL; BigQuery DataFrames exposes a Python API (bigframes); Gemini in BigQuery brings generative AI features, and Vertex AI integration enables calling remote models. BigQuery provides native vector search (VECTOR_SEARCH, CREATE VECTOR INDEX) and embedding generation (ML.GENERATE_EMBEDDING), supporting RAG scenarios.
BigQuery also distributes public datasets, including Google DeepMind’s WeatherNext weather forecasts. Billing follows an on-demand model (charged per bytes scanned) or a capacity model (slots/editions: Standard, Enterprise, Enterprise Plus).
Data & KnowledgeData & Knowledge ManagementData connectors, vector database integration, native vector search and data management (PII, provenance, synthetic data).
ApplicationsAI ApplicationsDomains and use cases this platform is best suited for — from RAG and fine-tuning to scientific research.
SecurityEnterprise SecurityCertifications, access controls and data-protection features essential for corporate deployments and cloud privacy compliance.
Developer EcosystemDeveloper EcosystemDeveloper resources: available SDKs, supported programming languages, and infrastructure features and model-deployment methods.
Pricing & Business ModelPricing & Business ModelBilling models (usage-based, provisioned throughput), resource limits and SLA parameters (uptime, support tiers).
Pricing models
Resource quotas