e6data aims to level the playing field for customers by negating the immense pricing power a handful of vendors enjoy due to deep barriers to entry and various new forms of compute ecosystem lock-in at different layers of the data stack.
In today’s digital-first landscape, enterprises rely on powerful data and AI capabilities to fuel innovation, enhance customer experiences, and optimize operations. However, they are set to spend a staggering $100b in 2024 on data intelligence platforms to derive value from their own data. Laser focused on the skyrocketing compute for data intelligence, e6data is announcing a $10m funding round as it offers superior processing efficiencies that halves the bill of enterprises seeking to analyze their vast troves of data. The series A funding round was led by Accel with participation from Beenext and others.
Data intelligence platforms are critical to enterprises seeking to extract value from their hundreds of data sources through essential workloads like data engineering, BI and analytics, machine learning, and now generative AI. As the imperative to extract maximum value from this data intensifies, enterprises face constant overruns and acute performance tradeoffs as they seek to effectively utilize their data. The total addressable market (TAM) 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.
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Vishnu Vasanth, co-founder and CEO commented: “This rapid increase has made data intelligence platforms the second largest IT spending category – behind only cloud spend for operational systems and application infrastructure. 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”.
However, as the spending grows, ROI concerns are reaching a boiling point. Enterprise technology leaders need a way to simultaneously increase performance and access new capabilities, while simultaneously controlling costs. They increasingly find there are no compelling alternatives to the status quo and are wary of emerging forms of ecosystem lock-in. “Legitimate ROI concerns stand in the way of enterprises realizing the full potential of data & AI. Moreover, organizations cannot freely move lakehouse table formats, data catalogs, compute providers, and cloud providers without adverse price-performance impacts, the need for data movement, and cumbersome application migrations. We aim to address this through our work at e6data” added Vishnu Vasanth.
To address these challenges, 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 down-time.
e6data 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.
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Data intelligence platforms like data lakehouses and warehouses are the foundation of all analytics and AI. At their core, they 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, 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 respect to cost, performance, concurrency handling, and scalability – particularly on compute-intensive heavy workloads that enterprises increasingly encounter as they operate at production scale.
e6data’s founding team saw an opportunity to address these gaps with a new engine architecture and distributed processing model that is disaggregated, decentralized, dynamic, and Kubernetes-native. The e6data engine outperforms leading commercial and open-source solutions across real-world heavy workloads and popular benchmarks: 5x higher performance, total cost of ownership (TCO) savings of more than 50%, and a truly format-neutral approach that negates ecosystem lock-in.
With a multi-disciplinary mix of distributed systems engineers, database builders, open source committers, and go-to-market leaders from Microsoft, ThoughtWorks, IBM DB2, Cisco, SAP, and Thoughtworks, the e6data team’s prior experiences in over 100+ large-scale data intelligence platforms gave them a first-hand view of the changing technology landscape, and the challenges facing enterprises as they scaled their data & AI needs.
e6data has already signed up publicly listed Fortune 500 enterprises as well as high growth companies as customers. It is anticipating explosive growth due to rising demand for compute-intensive heavy workloads across high-volume data products (e.g. customer-facing and business dashboards, reports), advanced analytics on near real-time data (personalization, fraud/risk, inventory planning), and production-grade generative AI applications (e.g. RAG for search, recommendation, customer support).
Data platform spend is already the top 2 CXO spend. However, the largest and fastest-growing spend drivers are typically from strategically important, nondiscretionary workloads.
According to Gartner, more than 80% of enterprises will be gen AI in production by 2026 which will further fuel the need for e6data’s high-efficiency, format-neutral compute infrastructure offering.
Rajaraman Santhanam, COO of Chargebee added: “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. 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 2 seconds. Other lakehouse engines we evaluated struggled to achieve this level of performance and scalability, despite being more resource intensive.”
While Rajeev Purohit, Head of Platform Engineering at Freshworks said: “We have been impressed with e6data’s performance, concurrency, and scalability on some of our heaviest use cases as part of our evaluation and co-creation of a next-generation data platform. Their team has been knowledgeable and responsive to our product needs through our collaboration on cutting-edge open lakehouse architectures.”
With its unique offering, e6data hopes to level the playing field for customers by negating the immense pricing power a handful of vendors enjoy due to various new forms of compute ecosystem lock-in at different layers of the data stack. Organizations cannot freely move lakehouse table formats, data catalogs, compute providers, and cloud providers without adverse price-performance impacts, the need for data movement, and cumbersome application migrations.
Shekhar Kirani, Partner at Accel, commented: “With GenAI, enterprises are seeing a surge in analytics use cases. Over the next few years, we expect every individual in an organization to be a power data consumer, implying a higher load on analytics and compute infrastructure. We believe e6data is primed to leverage and accelerate this movement.”