Qbeast announces $7.6m seed round
August 4, 2025
Qbeast, the next-generation data optimisation platform, has announced a $7.6 million (€6.5bn) seed round to fix that.
The seed round was led by Peak XV’s Surge (formerly Sequoia Capital India), with participation from HWK Tech Investment and Elaia Partners. The new capital will fund team expansion, 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.
Qbeast’s platform plugs directly into existing Delta, Iceberg, and Hudi tables and accelerates workloads by prioritising just the data required. Its multi-dimensional indexing can handle complex filters across columns like time, region, or customer segment – optimising 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.
To lead the next chapter of the company’s growth, Srikanth Satya, a cloud infrastructure veteran with decades of experience at AWS and Microsoft Azure, has been appointed as Qbeast’s CEO. His expertise in cloud-native architecture and strategic leadership will steer Qbeast through its next phase of global expansion.
“Data teams shouldn’t have to choose between speed, cost, and openness,” said Satya. “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.”
Today’s data lakes are massive, but not smart – and this is where the technical challenge lies. Everyone’s storing their data in open formats, but compute costs are exploding and most queries are painfully slow. Qbeast aims to solve this with drop-in indexing that delivers sub-second performance and cost savings, without locking users into a new stack. In production environments, Qbeast has already delivered query speedups of 2–6x and compute cost reductions of up to 70 per cent for workloads in finance, healthcare, and retail.
“There is an undesirable compute cost hidden in the data layout that has been highly neglected by the market for data lakehouses,” commented Flavio Junqueira, CTO of Qbeast and co-creator of Apache ZooKeeper and Apache BookKeeper. “Our technology enables customers across verticals to reduce or even eliminate such costs in a manner that embraces the openness of the data lakehouse stack and that is both engine and format neutral.”
“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.
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