Independent comparison for technology buyers. Updated May 2026.
Quick verdict: Choose Google BigQuery when running primarily on Google Cloud, when serverless on-demand pricing aligns with ad-hoc analytics, or when Vertex AI and Looker integration matters. Choose Amazon Redshift when AWS-native with deep S3, Glue, and SageMaker integration is preferred, or when Reserved Instance economics drive predictable workload procurement. The differentiator is serverless GCP warehouse versus provisioned-or-serverless AWS warehouse with S3 lakehouse depth.
| Criteria | Google BigQuery | Amazon Redshift |
|---|---|---|
| Rating | 4.5 / 5.0 (2,400 reviews) | 4.2 / 5.0 (2,100 reviews) |
| Architecture | Serverless, slot-based, fully managed | RA3 nodes with managed storage, Serverless |
| Cloud Deployment | Google Cloud only | AWS only |
| Pricing Model | Per-TB scanned + reserved slots | Node-hour, RPU-hour, storage |
| ML / AI | BigQuery ML + Vertex AI / Gemini | Redshift ML + SageMaker |
| BI Integration | Looker native, Power BI, Tableau | Power BI, Tableau, QuickSight |
| Lake Integration | BigLake, external Iceberg / Delta | Spectrum, Iceberg, Lake Formation |
| Streaming | Streaming inserts, subscriptions | Streaming ingestion, Kinesis integration |
| Best For | GCP estates, serverless analytics, AI | AWS estates, RI economics, S3 lakehouse |
Google BigQuery is a serverless cloud data warehouse on Google Cloud. Storage uses a proprietary columnar format; compute runs on slots from a shared on-demand pool or reserved capacity (Standard, Enterprise, Enterprise Plus editions). The serverless model removes warehouse sizing; analysts submit queries directly. BigQuery ML provides in-SQL machine learning; Vertex AI integration extends into Gemini and managed ML services. BigLake reads Iceberg and Delta tables in Cloud Storage.
Amazon Redshift is AWS-native with the RA3 architecture decoupling compute from storage on Redshift Managed Storage (backed by S3). Redshift Serverless removes node provisioning for spiky workloads. Redshift Spectrum queries S3 directly (Parquet, Iceberg). Redshift ML wraps SageMaker for in-database training and inference. Tight integration with Lake Formation, Glue, and Kinesis simplifies AWS-centric architectures.
For Google Cloud-centric estates, BigQuery typically wins on integration and developer experience. For AWS-centric estates, Redshift typically wins on AWS integration economics. Cross-cloud organisations frequently bring in Snowflake or Databricks to bridge clouds; see Snowflake vs Redshift for additional context.
BigQuery on-demand pricing is approximately $6.25/TB scanned; reserved slot editions are priced per slot-hour with annual commitments at significant discount. Active storage runs around $20/TB/month, long-term storage around $10/TB/month. Enterprise BigQuery spend commonly lands $200,000-$8M ARR.
Redshift RA3 dc2 instances start around $0.85/hour on demand, with 1-year and 3-year Reserved Instance discounts of 30-60%. Redshift Serverless is around $0.36/RPU-hour with an 8-RPU minimum. Storage on RMS runs about $0.024/GB/month. Enterprise Redshift spend typically lands $200,000-$8M ARR. AWS EDP can significantly reduce effective cost.
Choose Google BigQuery when the data estate is GCP-centric, when serverless on-demand or reserved slot economics fit usage patterns, when Vertex AI and Gemini integration drive AI strategy, or when Looker is the BI platform of record.
Choose Amazon Redshift when AWS-native integration with S3 (Spectrum, Iceberg), Glue, Lake Formation, Kinesis, and SageMaker is preferred, when Reserved Instance economics align with predictable workloads, or when AWS EDP terms make Redshift economical at consolidated AWS scale.
This Bigquery vs. Redshift comparison summarises the practical differences between the two options for enterprise buyers. The analysis covers pricing models, target customer size, deployment options, integration coverage, and customer-reported strengths. Use the related comparisons below to evaluate either product against other alternatives.