Database management system provider MariaDB plc announced today that it’s launching a unified cloud platform designed to aid in the rapid development of next-generation artificial intelligence applications.
Immediately available, the company’s MariaDB Enterprise Platform 2026 eliminates the complexity of building AI apps by unifying transactional, analytical and AI-related vector database engines into a single, secure source for data.
Additionally, MariaDB built in native retrieval-augmented generation or RAG, a technique that allows large language models to retrieve new information based on context, and embedded preconfigured AI copilots providing access to agentic AI at the database level.
“The future of applications is agentic,” said Chief Product Officer Vikas Mathur. “AI agents need to probe, analyze and transact in real time and at enormous scale. At the same time, agents need to be grounded in insights contained in enterprise data that is trapped in fragmented silos today.”
To support data retrieval for AI, MariaDB’s new enterprise platform incorporates what the company calls “RAG-in-a-box.” Typically, developers must create their own context retrieval pipelines to access accurate knowledge for large language models. Integrating this functionality directly into the database platform will save developers time and effort. This can also speed up the process of retrieving data behind the scenes.
MariaDB’s embedded RAG solution eliminates the need for embeddings — a process needed to prepare data for AI vector storage — and the need to maintain a vector database and specialized data retrieval pipelines. The underlying systems handle everything automatically, consistently and optimize for speed and accuracy.
The addition of AI copilots builds in the capability for AI agents to interact with stored data using natural language. These allow other intelligent agents to connect with data directly without the need for extra layers, and provide a system for developers to “chat” with data for insights and administration.
Available today in MariaDB Cloud, preconfigured agents include a developer and a DBA AI copilot. The developer copilot provides text-to-SQL capabilities, allowing developers to write text queries that return insights on data stored in the database. The DBA Copilot offers database administration features like performance tuning and error debugging to boost administrator productivity.
Alongside these updates, the company said its Enterprise Server 11.8, the core component at the heart of Enterprise Platform 2026, achieved 250% better performance compared to version 11.6. In addition, developers and engineers will gain a new enterprise management tool that offers comprehensive observability for MariaDB databases, along with enhanced monitoring and visual tools.
Exa: a unified high-performance analytics engine
Doubling down on the company’s dedication to large-scale data capabilities, MariaDB also announced a strategic partnership with Exasol AG, the provider of a high-performance analytics engine, to integrate the company’s data analytics service with the new unified AI enterprise platform.
“MariaDB customers can now deploy a truly converged database solution that supports the performance requirements of GenAI, real-time dashboards, and complex data science — all while maintaining data governance and strict cost control,” said Exasol Chief Executive Joerg Tewes.
The joint solution, MariaDB Exa, combines the two companies’ capabilities to create a service that provides developers and data engineers access to extremely large, complex query capabilities on multi-terabyte and petabyte-scale datasets.
Tewes extolled the high performance and cost efficiency of the Exasol Analytics Engine, the core capability within Exa, noting that independent benchmarks revealed it was 20 times faster than other cloud companies.
MariaDB stated that the new solution provides speeds that are over 1,000 times faster than traditional online transactional processing solutions, which are used to manage high volumes of small transactions. Industries such as online banking, ATMs and e-commerce order entry systems commonly require tracking and updating numerous small transactions, making real-time processing a necessity.
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