Bring Google AI directly to your AWS and Azure data

Historically, running advanced analytics or training ML models across clouds meant a cross-cloud tax: high egress fees, network latency, and fragile ETL pipelines. The borderless Lakehouse solves this with Cross-Cloud Interconnects. These private, dedicated links deliver consistent bandwidth and lower latency than the public internet, at a fraction of the cost of traditional cross-cloud connections. The borderless Lakehouse supports zero variable egress costs when accessing your data from AWS1. With Partner Cross-Cloud Interconnect pricing, you get predictable monthly costs with an SLA-backed connection.

This managed, private connectivity simplifies provisioning from 1G to 100G using a flat-rate, subscription-based model. In addition, intelligent cross-cloud caching in the borderless Lakehouse securely stores remote data fragments temporarily inside Google Cloud to eliminate repeated, costly transfers for subsequent ad-hoc or BI queries. These capabilities, combined with BigQuery's vectorized processing, BigQuery AI functions for multimodal analysis, Spark's Lightning Engine runtime, and scalable metadata storage, scale to petabytes of data without sacrificing performance and enable: In-place AI and machine learning: Apply powerful AI models and Gemini directly to AWS data and Azure without migration. Ingesting metadata and context right where it lives helps guarantee high-accuracy grounding and a faster time-to-market. Avoid the cost and delay of data copying: Query live data stored in other clouds directly and bring your data closer to your agents.

Unified analytics experience: Deliver consistent, hardware-optimized performance across BigQuery, Spark, or open-source engines by centralizing multi-cloud compute back to Google Cloud’s infrastructure.