Qbeast, the next-generation data optimization platform, announced it secured $7.6 million in a recent seed round, enabling the company to expand its team, broaden product support across more analytics use cases, and double down on the company's mission to make open data platforms faster, simpler, and more cost-efficient.
The seed round was led by Peak XV's Surge (formerly Sequoia Capital India), with participation from HWK Tech Investment and Elaia Partners.
Born out of research at the Barcelona Supercomputing Center, Qbeast's platform plugs directly into existing Delta, Iceberg, and Hudi tables and accelerates workloads by prioritizing just the data you need, according to the company.
Its multi-dimensional indexing can handle complex filters across columns including time, region, or customer segment—optimizing for both real-time and historical queries in a single table.
Unlike traditional partitioning or sort orders that work in single dimensions, Qbeast enables simultaneous filtering across any combination of data attributes. And it integrates with popular compute engines such as Spark, Databricks, Snowflake, DuckDB, and Polars without requiring teams to rewrite pipelines or adopt a new storage layer.
"Data teams shouldn't have to choose between speed, cost, and openness," said Srikanth Satya, CEO. "We built Qbeast to make high-performance analytics simple and accessible, without locking organizations into proprietary systems. In a world where data is growing faster than ever, we're here to ensure every company can turn that data into value on their own terms."
Unlike closed platforms that require vendor lock-in or significant rewrites, Qbeast plays natively with the tools data teams already use, serving organizations across finance, retail, healthcare and beyond—any team using open formats to power analytics, AI, or business intelligence at scale, the company said.
"We believe every organization, not just the tech elite, should be able to extract value from their data without incurring massive cloud costs or hiring a team of engineers to tune performance," added Satya.
Looking ahead, Qbeast plans to extend its platform with auto-tuning, adaptive indexing, and deeper engine support across cloud providers and use cases. “The goal: to become the default indexing layer for open Lakehouse architectures and unlock a future where data-driven innovation doesn't come at the cost of performance, scalability, or sanity,” the company said.
For more information about this news, visit https://qbeast.io.