Rockset Introduces New Instance Class, Continuing to Make its Search and Analytics Database Affordable and Effective

neub9
By neub9
2 Min Read

Rockset, the cloud-based search and analytics database, is introducing a new instance class designed to improve cost efficiency without compromising on high-intensity compute workloads. This latest instance class from Rockset aims to reduce compute costs by 30%, demonstrating the company’s commitment to providing affordable solutions for a wide range of applications.

According to Venkat Venkataramani, co-founder and CEO of Rockset, compute is a critical resource for databases that support complex applications, but it is also one of the most expensive. The new general purpose instance class is optimized for low-cost search and AI applications, further establishing Rockset as the most affordable search and analytics database on the market.

In addition to the cost reduction, the new instance class offers a lower entry price point and autoscaling compute functions based on workload demands. Venkataramani highlighted the increasing demands on compute due to GenAI and vector search applications, prompting the need for innovation to enhance price performance for customers.

The instance class is suitable for a variety of applications, including recommendation engines, customer-facing analytics, personalization layers, and other use cases with high compute needs. It features real-time indexing, full-feature SQL on various data types, compute-compute separation, and vector search capabilities for billion-scale similarity search in the cloud.

Rockset’s innovation momentum is supported by strategic partnerships and investments, including the backing of Hewlett Packard Pathfinder and the Connect with Confluent partnership. Paul Glaser, vice president of Hewlett Packard Enterprise, emphasized the increasing demand for real-time data analytics and expressed excitement about investing in Rockset’s advanced capabilities.

For more information about Rockset, visit https://rockset.com/.

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