Head-to-head · Analytical Data Store Software
Google BigQuery vs Teradata VantageCloud
Google BigQuery leads on 0 checks, Teradata VantageCloud on 0, and 1 is even. Who comes out ahead on the 1 yes/no, price and count check where we have data for both products. The editor score weighs everything else too.
Our verdict
- Highest scoreGoogle BigQuery · 9.4/10
- Free planboth
Google BigQuery scores higher on our rubric for analytical data store software: 9.4 against 9.0 out of 10; our editors rank them #1 and #4.
Google BigQuery is the better fit for teams building serverless analytics across data sources. Teradata VantageCloud is the better fit for enterprise lakehouse analytics and ML.
- Google BigQuery fits best
Teams building serverless analytics across data sources
- Teradata VantageCloud fits best
Enterprise lakehouse analytics and ML
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Side by side
| Feature | Google BigQuery 9.4/10 Visit ↗ | Teradata VantageCloud 9.0/10 Visit ↗ |
|---|---|---|
| At a glance | ||
| Editor score | 9.4 | 9.0 |
| Ranking | #1 in Analytical Data Store Software | #4 in Analytical Data Store Software |
| Best for | Teams building serverless analytics across data sources | Enterprise lakehouse analytics and ML |
| Pricing model | Free | Free plan + paid |
| Starting price | Free | $6 usage-based |
| Free plan | ✓ | ✓ |
| Free trial | — | — |
| Deployment | Cloud | Cloud |
| Platforms | Web | Web |
| Support | Docs, Live chat, Phone, Tickets, Community | Docs |
| Integrations | 10 integrations | 7 integrations |
| Built for | Small business, Mid-market, Enterprise | Enterprise, Mid-market |
| Features Google BigQuery 0/2 · Teradata VantageCloud 0/2 | ||
| SQL support | Not checked yet | Not checked yet |
| Cross-account data sharing | Not checked yet | Not checked yet |
| Specs | ||
| Deployment model | Cloud | Cloud |
| Storage architecture | Not checked yet | Not checked yet |
| Compute scaling | Not checked yet | Not checked yet |
| Free storage limit | Not checked yet | Not checked yet |
| Our review | ||
| Pros |
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| Cons |
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| Our verdict | Google BigQuery is a fully managed, serverless cloud data warehouse for organizations that need to store, query, and analyze large datasets. Its SQL analytics, data engineering, business intelligence, data science, and real-time analytics… Read the review → |
Teradata VantageCloud combines a relational database engine, elastic compute, cloud storage, analytics, and machine learning for organizations running data warehouse, lakehouse, and AI workloads. Its Lake offering supports Apache Iceberg… Read the review → |
Strengths and trade-offs
Google BigQuery — where it wins
- Scale storage and compute independently for different workloads
- Query external sources and ingest streaming data
- Use SQL for analytics and BigQuery ML workflows
Where it doesn't
- Usage-based query charges depend on data scanned
- Storage and ingestion can add separate charges
- Cloud deployment may not suit teams seeking another hosting model
Teradata VantageCloud — where it wins
- Supports Apache Iceberg and Delta Lake in cloud object storage
- Combines elastic compute with object, block, and backup storage
- Includes in-database ML, Bring Your Own Model scoring, and ModelOps
Where it doesn't
- Pricing is contact-sales, with example pricing limited to AWS US East
- AWS pricing examples exclude cloud-provider charges
- Published support channels list documentation
- Google BigQuery9.4/10 · Free plan
A flexible SQL analytics store with separate storage and compute and a free tier.
Visit Google CloudFull verdict → - Teradata VantageCloud9.0/10 · Free plan · pricing on request
A cloud lakehouse and analytics platform built for enterprise data and ML workloads.
Visit TeradataFull verdict →
More comparisons
- Amazon Redshift vs Teradata VantageCloud
- Oracle Autonomous Data Warehouse vs Teradata VantageCloud
- Teradata VantageCloud vs Alibaba Cloud AnalyticDB
- Teradata VantageCloud vs Apache Doris
- Teradata VantageCloud vs Microsoft Azure Data Explorer
All analytical data store software comparisons → · Full ranking →
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Last updated · How we research and update





