“Getting started with the proof of concept was simple with DataStax. We completed development and deployment in just 37 days,” said Prasad Prabhakaran, generative AI practice lead at esynergy. "We were able to get up and running with the entire ecosystem without needing extensive support from DataStax. The ease of use allowed us to focus on understanding how we could leverage the platform more effectively to meet our customers’ needs.”

"We created a recommendation engine generating 30 million+ suggestions monthly in just 30 days—a record time for building a recommendation engine,” Shabeer Muhammed, senior software engineer at U Digital. “DataStax Astra DB and AWS were pivotal in our success, helping us achieve our goal within a remarkably short timeframe.”

“We were an early believer in the cloud, so we got started with AWS early. At the same time, we also picked Apache Cassandra as our database of choice, as it promised availability and scalability. We were able to grow rapidly based on this combination,”

“For Dataworkz, having a vector database is important to harness large language models for either question answering systems insights and intelligent summarization. Astra DB on AWS vector capabilities is a really great addition for us.”

“By going serverless with Astra DB on AWS, we reduced the operational costs for our Cassandra database platform by an order of ten,” says Viswanath. “With Astra DB, we get high read and write throughput for large volumes of data, allowing us to rapidly deliver analytics and alerts for predictive maintenance, fuel savings, and more.”