Cloud Platform
Building enterprise AI on Google Cloud
BigQuery and Vertex AI sit close together by design. We use Google Cloud where analytics and AI need to operate directly on the same data.
How does Google Cloud support AI and analytics working together?
Google Cloud is a strong fit for AI systems where analytics and AI need to operate closely together, since BigQuery and Vertex AI are designed to work directly on the same underlying data. Divine AI Systems uses Google Cloud as the Cloud & Infrastructure layer for organizations with an existing BigQuery investment, or where combining large-scale analytics with AI model access in one platform reduces integration overhead.
Where It Fits
Where Google Cloud fits in the architecture
Google Cloud's value is the short distance between where your data lives and where your AI models run.
Cloud & Infrastructure
- Compute
- storage
- networking
Knowledge & Data
- BigQuery
- structured operational data
AI & Reasoning
- Vertex AI
- managed model access
Orchestration & Integration
- Cloud Workflows
- Pub
- Sub
Security, Monitoring & Governance
- IAM
- Cloud Audit Logs
What We Build
What we build on Google Cloud
The range of what a Google Cloud-based engagement typically includes.
BigQuery-Native Analytics AI
AI features built directly against data already living in BigQuery, without a separate export step.
Vertex AI Applications
Custom applications and agents built on Vertex AI's managed model access.
Large-Scale Data Pipelines
Ingestion and transformation pipelines feeding both analytics and AI from one data platform.
Predictive & Forecasting Models
Machine learning models trained and served directly against BigQuery data.
When It Makes Sense
When Google Cloud is the right choice
These are the conditions where Google Cloud tends to be the stronger fit.
BigQuery is already your analytics platform
Building AI directly against existing BigQuery data avoids a separate data-movement step.
Analytics and AI need to share the same pipeline
Workloads that blend forecasting, reporting, and generative AI benefit from one unified platform.
Large-scale structured data processing is central to the problem
BigQuery's scale suits data-heavy workloads that feed downstream AI models.
Tradeoffs
What to weigh before committing
These are the considerations we walk through with clients before recommending Google Cloud.
Smaller enterprise application ecosystem than some competitors
Organizations expecting deep pre-built enterprise app integrations may find more available on other platforms.
Best value depends on an existing or planned BigQuery investment
Without a genuine analytics-and-AI convergence need, the differentiator matters less.
Migration cost if data lives elsewhere today
Moving an existing data warehouse into BigQuery is a real project, not a side effect of adding AI.
Related Services
Services we typically pair with Google Cloud
Related Solutions
Business problems this often supports
FAQ
Questions about building on Google Cloud
We build on Google Cloud for client engagements. We describe that as technical experience, not a formal partnership or certification.
The strongest benefits come from an existing or planned BigQuery investment. Without that, the case for Google Cloud over another provider is weaker.
Yes — it's common to use Vertex AI for data-adjacent tasks while using another provider's model for a different part of the same system.
It depends on your existing data platform and requirements — we'll recommend based on your specific situation, not a general preference.
Already running on BigQuery?
Tell us about your data platform today, and we'll show you exactly where AI fits on top of it.