Yuki, a startup focused on optimizing data costs, has announced its launch from stealth mode with a $6 million seed investment. The funding round was led by Hyperwise Ventures and also included investors like VelocitX, Tal Ventures, Fresh.fund, and Yakir Daniel, the founder of Spot.io. The Israeli company is tackling a growing problem: most organizations simply spend more on infrastructure to handle increasing data and AI workloads, often in a way that wastes resources and drives up costs.

Why Traditional Data Infrastructure Is Struggling

Many companies still rely on a one-size-fits-all approach to their data infrastructure. They use the same compute resources for different types of workloads, from critical business queries to experimental AI models. As data volumes grow, this leads to massive inefficiencies, with teams competing for the same resources and no one managing the data itself—only the infrastructure around it. This mismatch causes higher costs and slower performance, especially as workloads become more complex and diverse.

Yakir Daniel, who participated in Yuki’s seed round, noted that the pain point the startup is solving is clear to him. Having built Spot.io before, he recognizes how AI is making data spending a board-level issue. Yuki is building a control layer that can optimize data costs across different platforms and workloads, helping organizations better manage their data infrastructure.

How Yuki’s AI-Powered Control Layer Works

At the core of Yuki’s technology is Yuki Fabric, an AI-driven model that acts as a unified control and automation layer. It supports popular data platforms like Snowflake, Google BigQuery, and Iceberg-based data lakes. This setup allows Yuki to oversee next-generation data architectures, continuously learning from workload behavior, service level agreements, and cost-performance tradeoffs. It then makes real-time decisions to optimize data processing and costs.

The control layer sits above different vendors, preventing duplication of infrastructure and reducing operational costs. It enforces SLAs across teams and workloads, ensuring efficient resource use. As more companies adopt Iceberg to separate storage from compute, Yuki provides the missing intelligence layer to govern how resources are consumed, making data infrastructure smarter and more adaptable.

This approach enables organizations to avoid the traditional reaction of simply increasing infrastructure spending. Instead, they can manage their data workloads intelligently, cutting costs while maintaining performance and reliability.

Real-Time Optimization Without Disrupting Workflows

One of Yuki’s key advantages is its ability to optimize data workloads in real time without requiring any code changes from users. Ido Arieli Noga, CEO of Yuki, explains that data is the most unmanaged resource in many organizations. The common response to data growth has been to pour more money into infrastructure, which is no longer sustainable, especially in an AI-driven world.

By making data infrastructure workload-aware and governed by an AI control layer, companies can significantly reduce costs. Yuki’s platform helps organizations scale more efficiently, avoiding unnecessary spending. This approach is especially important as data volumes and AI workloads continue to surge, demanding smarter management tools.

Overall, Yuki aims to transform how companies handle their data costs—moving away from the traditional model of simply adding more hardware to a smarter, AI-driven solution that optimizes resource use and saves money.

Inspired by

• https://justainews.com/companies/funding-news/yuki-raises-6m-to-cut-data-costs-by-42-using-ai-control-layer/