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Amazon RedShift vs Google BigQuery
Amazon RedShift and Google BigQuery are both Data Storage & Warehousing tools. Here is how they compare on pricing, free plans, AI integrations and platforms, and when each is the better fit.
Side by side
| Feature | Amazon RedShift | Google BigQuery |
|---|---|---|
| Category | Data Storage & Warehousing | Data Storage & Warehousing |
| Pricing | Pay-as-you-go | $0–2,000/mo |
| Free option | Yes | Yes |
| AI integrations | MCP server, Claude, ChatGPT | MCP server, Gemini |
| Best for | — | Marketing data warehouse, GA4 raw export, SQL analytics |
| Headquarters | — | Mountain View, US |
| Founded | — | 2010 |
| Links | Amazon RedShift listingVisit website (opens in a new tab) | Google BigQuery listingVisit website (opens in a new tab) |
Which to choose
Choose Amazon RedShift if
- want the choice between manually provisioned clusters and serverless auto-scaling
- query data directly across S3 data lakes and operational databases without loading it first
- use Amazon Bedrock for generative AI or connect via the ChatGPT Work and Claude Desktop plugins
- already run your infrastructure on Amazon Web Services and have internal AWS expertise
Choose Google BigQuery if
- need a marketing data warehouse for GA4 raw exports and SQL analytics
- want a purely serverless architecture that separates compute and storage without cluster configuration
- analyse unstructured text, images, audio, and video alongside structured tables
- need geospatial analytics with Google Maps Platform data or Apache Iceberg lakehouse support
Verdict
Amazon Redshift suits organisations already invested in AWS that want control over how their warehouse runs. It offers both manually configured clusters for steady workloads and a serverless option for variable demand, alongside direct querying across S3 data lakes. The main trade-off is that cluster configuration and query optimisation require specialised AWS knowledge, and moving data outside AWS can trigger egress charges. Google BigQuery fits marketing and analytics teams looking for a purely serverless warehouse, particularly for GA4 raw exports, multimodal data, and geospatial analysis. It removes infrastructure management by separating storage and compute, and includes built-in Gemini AI agents. However, scanning large or unoptimised datasets can drive up usage costs quickly, and both platforms carry a risk of ecosystem lock-in.
FAQ
- How do Amazon Redshift and Google BigQuery compare on pricing and free options?
Both platforms use usage-based pricing and offer a free option. Amazon Redshift operates on a pay-as-you-go model with a two-month free trial tier for new accounts. Google BigQuery provides a free monthly allowance for storage and query scanning, with typical usage pricing listed at $0 to $2,000 per month.
- How does infrastructure management differ between Redshift and BigQuery?
Amazon Redshift offers two deployment models: Provisioned clusters where teams manually configure compute capacity, and Redshift Serverless which scales automatically. Google BigQuery is entirely serverless and separates compute from storage, so teams never have to manage infrastructure or plan capacity.
- Do both data warehouses support SQL-based machine learning and generative AI?
Yes, both allow users to build and deploy machine learning models using SQL via Redshift ML and BigQuery ML. For generative AI, Redshift connects with Amazon Bedrock and offers MCP servers and a ChatGPT Work plugin, while BigQuery integrates with Google's Gemini models and provides a BigQuery MCP server.
- Which tool is better suited for marketing analytics and unstructured data?
Google BigQuery is specifically listed as best for marketing data warehousing and GA4 raw exports. It also supports multimodal analysis across unstructured text, images, audio, and video, whereas Amazon Redshift focuses on structured and semi-structured data.