Qbeast has raised $7.6 million in seed funding to scale its platform, which tackles one of the biggest inefficiencies in open data infrastructure: wasted compute power.
The funding round, led by Peak XV’s Surge, with backing from HWK Tech Investment and Elaia Partners, will support team growth, product development, and wider market reach.
As data volumes balloon and AI workloads push infrastructure to its limits, Qbeast’s mission is to reduce the overhead of open formats like Delta Lake, Iceberg, and Hudi, where up to 90% of resources are wasted scanning irrelevant data, according to Databricks.
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.
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 prioritising just the data you need.
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 like 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 deep technical 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 organisations 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 solves this with drop-in indexing that delivers sub-second performance and cost savings, without locking you into a new stack. In production environments, Qbeast has already delivered query speedups of 2–6x and compute cost reductions of up to 70% 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,” shared 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.”
The team behind Qbeast includes heavy hitters in distributed systems and open data. The company’s core technology is rooted in research conducted by Cesare Cugnasco, CSO of Qbeast and Paola Pardo during their time at the Barcelona Supercomputing Centre, where breakthrough work in multi-dimensional indexing laid the foundation for today’s platform.
Unlike closed platforms that require vendor lock-in or significant rewrites, Qbeast plays natively with the tools data teams already use, serving organisations across finance, retail, healthcare and beyond — any team using open formats to power analytics, AI, or business intelligence at scale.
“We believe every organisation, 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.
“We believe Qbeast is solving a fundamental challenge in the modern data stack. In a context of data volume explosion, their multi-dimensional indexing layer has the potential to become critical for every company moving to a lakehouse model”, said Juan Santamaría, CEO and managing partner at HWK TechInvestment.
“By empowering enterprises to unlock more value from their data with less complexity and expense, Qbeast aims to become the cornerstone indexing layer for modern data stacks,” said Sébastien Lefebvre, partner & deep tech investor at Elaia.
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 is 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.
