Citing privacy and security concerns over public large language models, Kinetica is adding a self-developed LLM for generating SQL queries from natural language prompts to its relational database for online analytical processing (OLAP) and real-time analytics.
The company, which derives more than half its revenue in the US with concentration in financial services, telco, logistics, automotive, and defense, claims that the native LLM is more secure, tailored to the database management system syntax, and is contained within the customer’s network perimeter.
With the release of its LLM, Kinetica joins the ranks of all the major LLM or generative AI services providers — including IBM, AWS, Oracle, Microsoft, Google, and Salesforce — that claim that they keep enterprise data to within their respective containers or servers. These providers also claim that customer data is not used to train any large language model.
In May, Kinetica, which offers its database in multiple flavors including hosted, SaaS and on-premises, had said that it would integrate OpenAI’s ChatGPT to let developers use natural language processing to do SQL queries.
Further, the company said that it was working to add more LLMs to its database offerings, including Nvidia’s NeMo model.
The new LLM from Kinetica also gives enterprise users the capability to handle other tasks such as querying time-series graph and spatial queries for better decision making, the company said in a statement.
The native LLM is immediately available to customers in a containerized, secure environment either on-premises or in the cloud without any additional cost, it added.
(The story has been updated to reflect new information about the company's customer base.)