Reference Architecture for Unified Multi-Cloud AI Data Platforms

Enterprises increasingly operate across multiple cloud ecosystems to support large-scale analytics, artificial intelligence (AI), and data-driven decision making. However, the rapid growth of specialized cloud services has created architectural fragmentation, where data ingestion pipelines, analytics workloads, machine learning platforms, and business intelligence tools operate across heterogeneous environments. This paper proposes a reference architecture for unified multi-cloud AI data platforms that integrates data ingestion, governed storage, analytics processing, and AI/ML services into a cohesive enterprise framework. The architecture is designed to support interoperability across major cloud ecosystems including AWS, Azure, Google Cloud Platform, BigQuery, and Snowflake while maintaining governance, portability, and operational consistency. The study analyzes architectural layers for data integration, lakehouse and warehouse storage models, and consumption patterns for AI services and business intelligence applications. It also introduces practical evaluation metrics for platform performance, data freshness, portability overhead, and cost efficiency in multi-cloud environments. Through a comparative analysis of leading cloud data platforms, the paper highlights how hybrid deployment strategies can balance platform-native capabilities with enterprise governance requirements. The findings demonstrate that effective enterprise data platforms do not rely on a single cloud provider but instead leverage unified architectural principles, governed data products, and reusable analytics pipelines to enable scalable AI-driven analytics across domains such as healthcare, FinTech, and retail. The proposed reference architecture provides guidance for organizations seeking to design resilient, interoperable, and governance-aware multi-cloud data platforms for enterprise AI workloads.