MariaDB is set to acquire GridGain Systems with the aim to deliver sub-millisecond data performance for agentic AI workloads. GridGain is the company behind the in-memory computing platform and developer of the open source project Apache Ignite. The deal comes at a time when technology vendors are rethinking the foundations of the modern data stack to support emerging agentic AI workloads.
Much of that rethink stems from what MariaDB describes as the “AI latency gap.” Many of the traditional architectures were designed decades ago. They are typically built for transactional applications and as a result struggle with rapid and iterative data access patterns required by AI agents. As enterprises move from experimentation to execution stage with AI agents, even small delays in accessing operational data can quickly compound across workflows. That is the latency gap MariaDB aims to solve with GridGain.
That solution is based on in-memory computing – an area where GridGain specializes. The company’s technology processes and stores data directly in memory across distributed clusters. They claim this is faster than repeatedly pulling it from disk-based storage layers. With frequently accessed datasets kept in memory, the aim is to reduce the delays that typically slow down traditional database architectures.
MariaDB’s role in this is to provide the durable transactional foundation behind that speed. In other words, MariaDB holds the durable data, while the in-memory layer makes that data much faster to access and work with.
Many organizations rely on a mix of data processing layers to achieve both speed and reliability. MariaDB argues that integrating in-memory computing directly with its relational database platform could reduce that architectural complexity while still delivering the performance required by emerging AI workloads.
With this acquisition, MariaDB aims to move beyond the traditional database vendor category and into a different weight class alongside the industry’s major platform players.
“Enterprises today cannot afford the latency introduced by siloed data architectures. With MariaDB and GridGain, enterprise customers will get a unified platform that provides them the best of both worlds, performance and scale without having to give up on durability,” says Lalit Ahuja, CTO of GridGain, Inc.
“The combined technology stack will unlock one of the key enablers for agentic enterprises: high-performance and reliable data processing that powers the next generation of AI applications.”
MariaDB claims that this acquisition comes after an 18 month campaign led by CEO Rohit De Souza to assemble the building blocks required to power the next generation of agentic workloads. This includes integrating vector search, cloud elasticity, and now extreme in-memory speed to the platform.
According to De Souza, “The rise of agentic workloads has placed unprecedented demands on enterprise infrastructure, causing requirements to explode and requiring a level of scale and sub-millisecond latency that traditional systems simply weren’t built to handle.”
He further added that the acquisition enables MariaDB to “provide a high-performance, scalable, open alternative to the rigid lock-in of Oracle and the fragmented complexity of hyperscalers.”
MariaDB has gone through several strategic shifts in recent years. Investment firm K1 Investment Management acquired the company in 2024 and installed a new leadership team as part of an effort to reposition the database vendor in a rapidly changing data infrastructure market.
The company has since expanded its platform through acquisitions, including Codership, the developer of the Galera Cluster technology used for high availability, and SkySQL, which helped extend MariaDB’s capabilities into database-as-a-service and cloud deployments.
Today MariaDB competes across a broad landscape that includes traditional database vendors like Oracle, hyperscale cloud providers and distributed data platforms such as Hazelcast, Redis and PostgreSQL, while newer entrants like VAST Data are building data architectures designed specifically for AI workloads.
(Volodimir Zozulinskyi/Shutterstock)
The origins of GridGain go back to 2007, when the company began developing distributed in-memory computing technology designed to process large volumes of data much faster than traditional database architectures. Its core technology later became the open source project Apache Ignite, now widely used as a distributed data grid for real-time applications.
If agentic AI becomes a mainstream enterprise architecture, the speed at which data systems can respond may become a defining factor. MariaDB is betting that combining its database platform with GridGain’s in-memory technology will help close that gap.
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The post MariaDB to Acquire GridGain to Tackle the AI Latency Gap appeared first on BigDATAwire.
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Author: Ali Azhar