UPDATED 12:30 EST / DECEMBER 13 2023


Unlocking the power of AI: DataStax’s flexible database for the future

In the development of artificial intelligence technology, DataStax Inc. has made it its mission to provide a cloud-native vector database to help catalyze the next wave of AI innovations. The true power of DataStax’s endeavor lies in its ability to seamlessly integrate with the data at the heart of every business.

“Once you get down to it, it’s only as powerful [as it is] to your business. The impact to your business is going to be dependent on it working with the data,” said Ed Anuff (pictured), chief product officer of DataStax.

Anuff spoke with theCUBE industry analysts Dave Vellante and John Furrier at the “Supercloud 5: The Battle for AI Supremacy” event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed creating flexible databases for AI technology and the process of implementing it into a business.

Augmenting databases to support AI

DataStax’s Astra DB is a cloud database built on top of Apache Cassandra, a widely used open-source database. The company recently empowered it to do vector search, which is essential for integrating and supporting AI programs.

“You have all that power, you have all that scale, but all the power of being able to do vector similarity search and vector is … the native language of LLMs,” said Anuff.

Astra DB allows users to perform Retrieval-Augmented Generation, or RAG, a method for checking the accuracy and reliability of generative AI models that draw on external sources. This helps detect when AI models are hallucinating, or making wrong guesses and correcting them.

“The key piece for businesses, how do I make AI work with my data?,” said Anuff, “It’s going to be low latency. You’ve got to be able to go and deal with the entirety of the data that runs your business, making it available to these language models.”

Amidst the frenzy around AI models, companies are trying to determine the most cost-efficient option for their business. There is no one right answer, according to Anuff.

“Right now you’re trying to find that sort of goldilocks zone of how do I get the best possible relevance at a cost that lets me go and bring this out to … my users,” he said, “But I need to be able to push a lot of data through this. And these LLMs are not all created equal in terms of cost.”

DataStax offers users a flexible data architecture that can adapt to their changing business needs, claims Anuff. The vector search is a key component of this flexibility because it can translate different databases into a language readable to AI.

“What we try to do from an architecture standpoint is let you go and easily switch between them in any situation,” said Anuff. “You’re bringing your data to the model, but we’re giving you that optionality to tune it so that it gives you the right relevancy, the right accuracy at the right cost that makes this possible for whatever it is you’re trying to do with your business.”

The timeline for production on AI technology looks surprisingly fast, according to Anuff, who estimates that while some companies have gone into production this year, most will be doing so over the next several quarters. Either way, the appetite for AI is huge.

“In terms of the pace of innovation this year, it’s been unlike any other time,” he said.

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the “Supercloud 5: The Battle for AI Supremacy” event:

Photo: SiliconANGLE

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