Breaking Through AI Workload Bottlenecks: How OceanBase’s AI-Native Capabilities Empower Enterprise

Enterprise-grade RAG and AI Agent applications face critical bottlenecks in production: siled data systems, complex tech stacks, high retrieval latency, and exploding vector storage costs.

This session focuses on how an AI-native distributed database removes these constraints at scale. We share real-world enterprise practices of OceanBase in powering mission-critical RAG and Agent systems, including China Unicom’s ChatDBA and a leading enterprise AI assistant. We will dive into OceanBase’s multi-model architecture that unifies structured, full-text, and vector data in one engine—eliminating the need for dedicated vector databases and simplifying deployment. We explain how its proprietary VSAG indexing and binary quantization reduce memory costs by up to 95% while maintaining high recall and query performance. We also cover native SQL-based AI functions, cloud-native elasticity, and integration with mainstream AI frameworks and MCP protocols.