BELLEVUE, Wash. (BLOOM) — As data volumes surge and cloud costs climb, Qbeast, a data infrastructure company focused on analytics performance, raised $7.6 million to advance its mission: making open data platforms faster and more cost-effective.

The seed round was led by Peak XV’s Surge program, with additional funding from HWK Tech Investment and Elaia Partners. The company, which spun out of research conducted at the Barcelona Supercomputing Center, is building what it calls a “multi-dimensional indexing layer” that plugs into popular data formats like Delta Lake, Apache Iceberg and Apache Hudi.

Qbeast’s software is designed to reduce the amount of data scanned during queries, aiming to improve performance and lower compute costs, often by as much as 70% in production environments, according to the company.

“Today’s data lakes are massive, but not smart,” said Srikanth Satya, CEO of Qbeast and a former cloud executive at AWS and Microsoft. “We’re helping organizations avoid waste and unlock faster performance—without vendor lock-in or rewriting pipelines.”

The company’s technology is built for compatibility with major analytics engines including Spark, Snowflake, and Databricks. Its indexing system prioritizes relevant data across multiple attributes, such as time, region, or customer segment, enabling faster access to both real-time and historical data.

Qbeast plans to use the funding to expand its engineering team, build out auto-tuning and adaptive indexing features, and strengthen support for enterprise deployments.

The investment comes as more organizations look to open “Lakehouse” architectures for managing AI and business intelligence workloads. But with greater flexibility comes higher costs, especially when inefficient data access strains compute budgets.

“We believe Qbeast is solving a fundamental challenge in the modern data stack,” said Juan Santamaría, CEO of HWK Tech Investment, one of the round’s backers. “Their indexing layer has the potential to become critical infrastructure for any company moving to a Lakehouse model.”

With demand for scalable, open data systems showing no signs of slowing down, Qbeast’s leadership sees this as a critical moment. “We’re building infrastructure that lets teams get more out of the systems they already use,” Satya said. “This is about performance, but it’s also about choice.”