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Abstract
Generative AI platforms have rapidly evolved from experimental, model-centric applications into production-grade systems that operate under strict latency, scalability, and reliability constraints. These platforms depend on continuous access to heterogeneous data artifacts such as conversational state, retrieval context, feature snapshots, tool outputs, and operational metadata. As interaction volumes grow, the persistence layer becomes a critical determinant of system performance and user experience.
This paper examines the role of cloud-native NoSQL databases as foundational persistence infrastructure for large-scale generative AI platforms. We focus on data modeling strategies, access patterns, and lifecycle considerations that support high-throughput, low-latency workloads while accommodating evolving application requirements. Rather than emphasizing model architectures or application-level intelligence, the discussion centers on how scalable NoSQL systems enable reliable state management, session continuity, and metadata persistence in production environments.
We present a taxonomy of generative AI data categories, analyze common read–write patterns observed in interactive AI systems, and outline design trade-offs across key NoSQL paradigms including key-value, wide-column, and document-oriented stores. Empirical considerations emphasize tail-latency behavior, horizontal scalability,
