OceanBase’s AI Database: Building a Unified Foundation for the Next Generation of Enterprise AI

Enterprise AI adoption has reached a critical inflection point. While large language models have advanced rapidly and largely solved the core reasoning challenge, real-world deployment in companies has plateaued. The primary bottleneck is no longer model intelligence — it is data.

As AI agents move beyond chat interfaces into actual system execution and decision-making, they require continuous, trusted, real-time access to enterprise context. At the same time, business data has become increasingly multimodal — spanning structured records, documents, images, video, audio, logs, and vector embeddings.

Traditional fragmented architectures, built around separate data lakes, warehouses, and operational databases, struggle to deliver the unified, consistent, and governed context these agents need.

This shift is forcing a fundamental rethink of data infrastructure. The industry now recognizes that differentiation in AI applications is moving to the data layer. AI databases are emerging as a new frontier in foundational software, with vendors taking different paths: some extending data lakes, others enhancing search and semantic capabilities, and a few — like OceanBase — rebuilding capabilities from the database kernel itself. The real divergence lies in how these systems interpret the way AI will actually use data: how it will be organized, invoked, and leveraged for business decisions.

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The LakeBase Approach: Data Lake Meets Database

OceanBase addresses these challenges with its LakeBase architecture — a unified engine that combines the openness and massive scale of data lakes with the transaction processing, strong consistency, real-time serving, and reliability of traditional databases. This integration allows structured, unstructured, and vector data to be managed, processed, searched, and served within a single, strongly consistent foundation.

By extending the proven strengths of its financial-grade database — transaction consistency, high availability, elasticity, and millisecond-level recovery — to data lake and multimodal scenarios, OceanBase eliminates the fragmentation of multi-system setups. Four non-negotiable enterprise requirements remain central: consistency, scalability, reliability, and real-time performance.

The result is a data foundation specifically designed for AI workloads, where agents can reliably access memory, context, state, and business data without juggling disparate systems.

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A New Product Portfolio for AI-Era Data Management

OceanBase AI Database brings this architecture to life through three tightly integrated offerings:

• OceanBase Lakebase serves as the core data engine. It handles ingestion, processing, search, and serving of multimodal data in one place, giving organizations a solid foundation for AI applications.
• OceanBase DataStudio acts as the data production, governance, and services layer built on Lakebase. It covers the full lifecycle — from ingestion and orchestration to semantic modeling and collaboration with AI agents — turning scattered data silos into secure, callable services.
• OceanBase DataPilot functions as an intelligent business analytics agent. Non-technical users can generate reports, dashboards, and trusted answers through natural language, making data intelligence accessible across the organization.

Together, these components aim to reduce overall Total Cost of Ownership by 30–50% in many scenarios by removing redundant systems and complexity.

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OceanBase’s Proven Foundation

OceanBase is not new to mission-critical environments. Launched in 2010 to handle the extreme demands of China’s “Double 11” (Singles’ Day) shopping festival, it has spent 15 years enduring the toughest stress tests in the financial sector. It now powers more than 400 financial institutions and has held the top position in China’s financial distributed database on-premises deployment market for two consecutive years.

It remains the only database globally to have achieved top rankings in both the TPC-C (transaction processing) and TPC-H (analytical) international benchmarks. Its capabilities — zero data errors, uninterrupted operation, and rapid fault recovery — translate directly to the needs of the AI era.

OceanBase has grown to serve over 4,000 customers across multiple countries and regions. Its fintech footprint is particularly strong, with more than 100 fintech customers, including over 20 e-wallets (such as TNG Digital in Malaysia and GCash in the Philippines), 50 payment services, and 30 innovative fintech firms. These customers collectively reach more than 1.3 billion end users.

The same engineering rigor developed in high-stakes financial systems is now being applied to multimodal data and AI agent workloads.

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From Follower to Co-Definer

The AI database space is still in its formative stage — rules and architectures are not yet set in stone. This creates an opportunity for companies with deep, battle-tested expertise to help shape the next paradigm rather than simply follow existing ones.

OceanBase sees its role evolving from a successful follower in traditional database markets to an active participant in defining how AI interacts with enterprise data at scale. By unifying data lake openness with database-grade guarantees in a single engine, the company aims to provide the consistent, real-time, multimodal context that modern AI agents and enterprise applications require.

As CTO Charlie Yang has noted, databases must evolve “from systems of record into trusted context engines for AI.” OceanBase’s LakeBase architecture and AI Database portfolio represent a concrete step in that direction — one grounded in 15 years of real-world, high-stakes experience.

The coming years will show which approaches best meet the demands of production AI. OceanBase is betting that a deeply integrated, database-first unification of lakes and operational systems will prove to be a winning foundation.

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For more information, visit: https://www.oceanbase.com/