Enterprise data analytics firm e6data has secured a $10 million funding round. The series A was led by Accel, with participation from Beenext and others. It comes at a time when the total addressable market for data and AI solutions is slated to touch $230 billion in 2025, with 60% of CXOs expecting to increase their spend over the next year.
“This rapid increase has made data intelligence platforms the second largest IT spending category – behind only cloud spending for operational systems and application infrastructure, e6data co-founder and CEO Vishnu Vasanth said. “It’s fueling the meteoric rise of data warehouse and data lakehouse companies such as Snowflake and Databricks, and the rapid growth of corresponding offerings from AWS, Azure, and Google Cloud.”
Vasanth said that spending surge comes with ROI concerns. Enterprise technology leaders need a way to simultaneously increase performance and access new capabilities while simultaneously controlling costs.
“Legitimate ROI concerns stand in the way of enterprises realizing the full potential of data and AI,” Vasanth noted. “Moreover, organizations cannot freely move lakehouse table formats, data catalogues, compute providers, and cloud providers without adverse price-performance impacts, the need for data movement, and cumbersome application migrations.”
e6data has developed a new breed of “compute engine” for data intelligence platforms that helps enterprises amplify ROI on their existing platforms and architectures and escape ecosystem lock-in, all with zero friction to adoption in the form of zero data movement, zero application migration, and zero downtime. The company plans to expand access to its Design Partner Program, which offers the e6data solution as a managed service for the heaviest or most pressing use cases of enterprise customers, complete with production support and professional services.
Data intelligence platforms like data lakehouses and warehouses use distributed “compute engines,” whether open-source or vendor-backed, for every form of processing spanning ingestion, transformation, dashboards, reports, ML model training, and inference, as well as RAG-based generative AI applications. However, e6data said existing compute engines are built on monolithic architectures with centralized components for most aspects of a query or job’s life cycle. This creates challenges with cost, performance, concurrency handling, and scalability – particularly on compute-intensive heavy workloads that enterprises increasingly encounter as they operate at production scale.
The e6data team brings experience from Microsoft, ThoughtWorks, IBM DB2, Cisco, SAP, and Thoughtworks. The company said it has already signed up publicly listed Fortune 500 enterprises as customers.
According to Gartner, more than 80% of enterprises will have gen AI in production by 2026, further fueling the need for e6data’s high-efficiency, format-neutral compute infrastructure offering.
“We’ve been collaborating with e6data across several internal and external-facing analytics use cases, all built on Chargebee’s multi-purpose, scalable data lakehouse platform,” Chargebee COO Rajaraman Santhanam said. “We are seeing exciting opportunities to innovate for our customers. We have successfully supported concurrencies of over 1,000 QPS on near real-time (NRT) data and complex queries while maintaining client latencies of less than two seconds.”
