MariaDB Community Server Q2 2026 maintenance releases. MariaDB is pleased to announce the immediate availability of MariaDB Community Server 11.8.7, 11.4.11, 10.11.17, and 10.6.26 maintenance releases. See the release notes and changelogs for additional details on each release and visit mariadb.com/downloads to download. Release notes and changelogs. MariaDB Community Server 11.8.7. MariaDB Community Server 11.4.11. MariaDB Community Server 10.11.17.

MariaDB completes GridGain acquisition to power the next generation of agentic AI. March 24, 2026 Press play to listen to this content MILPITAS, Calif. and DUBLIN, March 24, 2o26 - MariaDB plc today announced the successful completion of its acquisition of GridGain Systems, Inc., the pioneer of in-memory computing and the force behind Apache Ignite. This strategic acquisition marks MariaDB's move towards delivery of an AI-Ready Operating Platform, a unified system designed to handle the extreme-velocity data requirements of autonomous AI agents. As enterprises transition from simple AI assistants to autonomous agentic systems that reason and act, traditional data layers are reaching a breaking point. By integrating GridGain's in-memory technology, MariaDB now provides a single, high-velocity, persistent grounding layer that supports the entire AI lifecycle - from real-time data ingestion to complex reasoning. "For the last 18 months, we have been building MariaDB for the agentic era," said Rohit de Souza, CEO of MariaDB. "By bringing GridGain into the fold, we are delivering a unified platform that does the heavy lifting for the enterprise. We are removing the friction of manual data assembly and defining the high-velocity grounding layer that AI agents need to be truly useful - all backed by integrated support from a single company." Solving the Agentic Gap The urgency for this platform is backed by industry shifts. Gartner predicts, "40% of enterprise applications will feature task-specific AI agents by 2026 - up from less than 5% in 2025."[1] Without a high-quality data foundation, "IDC warns that by 2027, companies that fail to establish high-quality, AI-ready data foundations will suffer a 15% productivity loss as generative and agentic systems falter" (IDC FutureScape 2026; Category: Worldwide Agentic Artificial Intelligence; Prediction 1). MariaDB's new platform addresses this inflection point by consolidating previously fragmented layers: * Unified transactions & analytics: Run real-time analytics alongside live transactions on the same platform without the friction of ETL (extract, transform, load). * In-memory velocity: Leveraging GridGain's expertise to provide sub-millisecond response times for agentic workloads. * Natively AI ready: Built-in vector capabilities to store, index and query embeddings, along with MCP server and integrations to AI frameworks, essential for building RAG workloads. * Global scale: Technology to support AI deployments across hybrid and multicloud environments, with instant scaling. The Agentic Shift: No Assembly Required Instead of forcing developers to manually stitch together separate databases for memory, vector search and transactions, MariaDB now offers a "single pane of glass" for data. This reduces complexity, lowers total cost of ownership (TCO) and allows developers to focus on building agent logic rather than managing backend bottlenecks. "Building for this level of scale today is like trying to build a high-speed machine out of a bucket of LEGOs - you have the pieces, but none of the pieces were meant to fit together under that kind of intensity," said Vikas Mathur, chief product officer at MariaDB plc. "At AI-speed, the window for a response shrinks. A data platform like MariaDB no longer has seconds; it has single-digit milliseconds to deliver answers to agents. By providing a platform with a high-speed in-memory 'baseplate' already built-in, we eliminate the friction of manual assembly. We are giving developers a unified grounding layer that can handle the massive scale these agents demand." Looking Ahead: The Distributed Future GridGain's technology provides the foundation for the next phase of MariaDB's evolution: a globally distributed data layer. As AI agents become autonomous and dispersed, the data they rely on must be as resilient and localized as the agents themselves. By extending the MariaDB Enterprise Platform into a distributed architecture, MariaDB ensures this grounding layer can scale across any region without sacrificing the sub-millisecond speed that machine-velocity workflows demand. This is a significant acceleration of MariaDB's commitment to a world where machine-speed is the baseline. About MariaDB MariaDB seeks to eliminate the constraints and complexity of proprietary databases, enabling organizations to reinvest in what matters most - rapidly developing innovative, customer-facing applications. Enterprises can depend on a single complete hybrid database platform for all their needs, that can be deployed in minutes for transactional, analytical, hybrid and AI use cases. Trusted by organizations such as Deutsche Bank, DBS, Nokia, Red Hat, Samsung and VirginMedia O2 - MariaDB delivers customer value without the financial burden of legacy database providers. For more information, please visit mariadb.com.

MariaDB has agreed to acquire GridGain, the developer behind Apache Ignite open-source in-memory computing technology, combining relational database products with in-memory data grid software to target low-latency AI and real-time applications. The deal addresses emerging "agentic" AI systems requiring sub-millisecond latency and fresh data access. MariaDB positions the combined platform as an alternative to Oracle and hyperscaler cloud providers, offering unified transactional, analytical and AI workloads across hybrid environments. GridGain's customers include American Express, Barclays, BNP Paribas, Citi, Deutsche Bank, HPE, Nokia, Verizon and American Airlines. The companies did not disclose financial terms. MariaDB CEO Rohit De Souza said the acquisition enables "a high-performance, scalable, open alternative to the rigid lock-in of Oracle and the fragmented complexity of hyperscalers". The transaction is subject to customary closing conditions.