GridGain Enables Real-Time AI with Enhanced Vector Store, Feature Store Capabilities

GridGain announced GridGain for AI, which enables organizations to use GridGain as a low-latency data store for real-time AI workloads.

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GridGain for AI can simplify the path from AI experimentation to delivery and execution, so businesses can confidently accelerate their AI deployments knowing that GridGain will ensure the performance, scale, and access controls they need to reliably process their data.

Real-time AI requires low-latency data access to ensure fast retrieval of inputs, such as features and embeddings for inference, enterprise- and user-specific context to augment LLM queries, prediction caching to reduce computation, and dynamic model loading. Typically, multiple systems for feature stores, caches, and model repositories are used, adding complexity and latency. GridGain for AI eliminates this by unifying these capabilities into a single, distributed platform, delivering ultra low-latency performance, seamless scalability, and reduced integration overhead to streamline deployments and improve overall system efficiency for modern AI applications.

“Organizations in every industry see the promise of AI and are moving toward implementation, but the benefits they seek won’t materialize if they do not have a robust, scalable, real-time data ecosystem powering their AI workloads,” said Lalit Ahuja, CTO, GridGain. “GridGain simplifies this data ecosystem, unifying feature stores, prediction caches, model repositories, and vector search into a single platform to reduce complexity, lower costs, and accelerate AI deployment.”

GridGain for AI accelerates both predictive AI and GenAI use cases:

• Predictive AI – GridGain for AI can be used as a feature store – including extracting features in real time from streaming or transactional data – or as a predictions cache. It can serve pre-computed predictions or execute predictive models in real time.

• GenAI – GridGain for AI can serve as the backbone for Retrieval-Augmented Generation (RAG) applications, enabling the creation of relevant prompts for language models using all necessary enterprise data. GridGain provides storage for both structured and unstructured data, with support for vector search, full-text search, and SQL-based structured data retrieval. It integrates with open-source and publicly available libraries (LangChain, Langflow) and language models.

Resource:
Blog: Using GridGain for AI as a Unified Data Store for Online AI Applications