By Shubhangi Chowdhury
California-based Eventual has raised $20 million in Series A funding to build next-gen data infrastructure tailored for the AI era. The funding round was led by Astasia Myers from Felicis, with backing from Microsoft’s M12 Ventures and Citi.
The first seed funding was led by Brittany Walker from CRV and Timothy Chen from Essence VC. Eventual plans to use the fresh capital to grow its team and accelerate product development.
As AI expands beyond just text, demand for Daft has been picking up fast. The company was founded in early 2022—almost a year before ChatGPT launched—at a time when most people hadn’t yet recognized the gap in data infrastructure for AI. They rolled out the first open-source version of Daft later that same year and are now preparing to launch their enterprise product in Q3.
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Founded by Sammy Sidhu and Jay Chia, Eventual’s Python-native data processing engine Daft is built to handle modern multimodal AI workloads. Daft is already in use by companies such as Amazon, CloudKitchens, and Together AI.
Sidhu announced in a LinkedIn post that the company has raised a total of $30 million combining seed and Series A funding.
The initial idea for Daft came while Sidhu and Chia were working on self-driving cars at Lyft, where they struggled to manage massive amounts of unstructured data—like text, images, video, and audio—without a unified processing tool. Since the right tools didn’t exist, they decided to build one themselves. “Jay Chia and I founded the company 3 years ago in my basement after hitting the same wall every AI team faces: trying to process images, video, and documents at scale with tools designed for ad clicks and bank transactions,” mentioned Sidhu.
With the growing need to make sense of massive, unstructured data, Daft was designed to handle that complexity with ease and flexibility.
To meet rising demand and take things to the next level, Eventual is now putting its new funding to work by launching Eventual Cloud—a commercial platform built to support large-scale AI workloads with reliability and security at its core. Alongside the product launch, the California-based startup is also growing its team, actively hiring talent across key functions.
As generative AI continues to evolve, Eventual sees itself becoming the foundational layer for teams working with messy, real-world data—enabling them to move faster, scale smarter, and focus on building instead of constantly reinventing infrastructure. “Daft improved Amazon’s most critical data job efficiency by 24% and has saved them 40,000 years of compute time annually and Together AI replaced custom Ray/Polars pipelines with simple Daft queries for 100TB+ datasets while being 10x faster,” said Sidhu in a LinkedIn post.
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Eventual has drawn support from both early believers—like Brad Flora (Y Combinator), Shruti Gandhi (Array Ventures), Raymond Tonsing (Caffeinated Capital), and Jeremy Fiance (The House Fund)—and strategic investors including Microsoft’s M12 and Citi.