GridGain for AI powers faster and more scalable predictive AI and generative AI use cases
FOSTER CITY, Calif., Feb. 4, 2025 /PRNewswire/ — GridGain®, provider of the leading real-time data processing and analytics platform, today announced GridGain for AI, which enables organizations to use GridGain as a low-latency data store for real-time AI workloads. 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, GridGain CTO. “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 generative AI (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.
For a deeper dive into the features and capabilities of GridGain for AI, read this blog post by GridGain’s Director of Product Management, Stanislav Lukyanov.
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About GridGain
GridGain is a real-time data processing and analytics platform from the original creators of Apache Ignite. It enables a simplified and optimized data architecture for enterprises that require extreme speed, massive scale, and high availability from their data ecosystem. GridGain’s distributed memory-first architecture and colocated compute deliver data processing and analytics at millisecond latencies, with configurable disk-based persistence for added durability. GridGain is trusted by companies like Citi, Barclays, American Airlines, AutoZone, and UPS to accelerate their existing applications, speed operational analytics and fraud detection, train machine learning models for AI, and provide fast-access data hubs. To learn more, please visit www.gridgain.com.
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Press release URL: https://www.gridgain.com/company/news/press-releases/gridgain-enables-real-time-ai-vector-store-feature-store
GridGain is a trademark or registered trademark of GridGain Systems, Inc. All other product and company names herein may be trademarks of their registered owners.
SOURCE GridGain