GridGain, provider of a leading real-time data processing and analytics platform, is introducing GridGain for AI, enabling organizations to use GridGain as a low-latency data store for real-time AI workloads.
According to the company, 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.
"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 such as:
- 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 more information about this news, visit www.gridgain.com.