Independent comparison for technology buyers. Updated May 2026.
Quick verdict: Choose Google BigQuery for GCP-native serverless analytics with Vertex AI and Looker integration. Choose Azure Synapse Analytics for Azure-native dedicated and serverless SQL with Power BI integration on a Microsoft Fabric path. The differentiator is serverless GCP warehouse with first-class AI tie-ins versus an Azure-coupled analytics stack moving to Microsoft Fabric.
| Criteria | Google BigQuery | Azure Synapse Analytics |
|---|---|---|
| Rating | 4.5 / 5.0 (2,400 reviews) | 4.2 / 5.0 (1,600 reviews) |
| Architecture | Serverless, slot-based | Dedicated SQL pool, serverless SQL pool, Spark |
| Cloud Deployment | Google Cloud only | Azure only |
| Pricing Model | Per-TB scanned + reserved slots | DWU-hour, per-TB scanned, vCore-hour |
| AI | BigQuery ML + Vertex AI / Gemini | Azure ML, Synapse ML, Azure OpenAI |
| BI Integration | Looker, Power BI, Tableau | Power BI native, DirectQuery |
| Future Direction | Native AI integration with Gemini | Migration path to Microsoft Fabric |
| Lake Integration | BigLake, Iceberg / Delta | ADLS Gen2 via serverless SQL pool |
| Best For | GCP estates, serverless, AI | Azure SQL DW workloads, Fabric path |
Google BigQuery is GCP-native serverless analytics with on-demand or reserved slot pricing. BigQuery ML brings in-SQL machine learning; Vertex AI integration extends into managed Gemini models and ML pipelines. Tight integration with Looker and Dataform creates a coherent GCP analytics stack. BigLake extends BigQuery to read Iceberg and Delta in Cloud Storage.
Azure Synapse Analytics is Microsoft's Azure analytics platform combining a dedicated SQL pool (MPP warehouse), serverless SQL pool (per-TB-scanned queries over ADLS), Spark pools, and Synapse pipelines. Power BI integrates natively. Microsoft positions Fabric as the unified successor, and most new investment targets Fabric. Synapse remains supported with a documented migration path.
For Azure estates, the medium-term comparison increasingly becomes BigQuery versus Microsoft Fabric rather than Synapse. See Snowflake vs Fabric, Microsoft Fabric vs Synapse, and additional options in the data analytics category.
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 around $20/TB/month, long-term storage around $10/TB/month. Enterprise spend typically lands $200,000-$8M ARR.
Azure Synapse dedicated SQL pool runs around $1.20/hour per 100 DWU on demand, with reserved discounts. Serverless SQL pool is around $5/TB scanned. Spark pools are billed per vCore-hour. Storage uses Azure Data Lake Storage Gen2 standard rates. Enterprise Synapse spend typically lands $200,000-$6M ARR with Microsoft EA discounts often shaping the comparison.
Choose Google BigQuery when standardised on Google Cloud, when Vertex AI integration matters, when serverless query economics fit ad-hoc analytics patterns, or when Looker is the BI platform of record.
Choose Azure Synapse Analytics when existing dedicated SQL pool workloads must continue, when Power BI dependency is high, or when migration to Microsoft Fabric is the planned direction and Synapse is the interim platform.
This Bigquery vs. Synapse 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.