$6M Boost for Yuki: Taming Cloud Data Costs in the AI Era
January 24, 2026, 3:54 pm
Yuki, a New York City startup, secured $6M in seed funding, led by Hyperwise Ventures. The company is transforming data cost optimization for platforms like Snowflake, BigQuery, and Iceberg-based data lakes. Its innovative Yuki Fabric platform operates in real-time, automating workload management and query routing. This smart system dynamically adjusts execution based on system behavior. It dramatically reduces costs and boosts performance in data environments increasingly driven by AI. Yuki offers crucial control, addressing the escalating, unpredictable cloud budgets many organizations face. With zero-change deployment and a pay-for-savings model, customers achieve significant financial gains. The investment targets R&D expansion in Israel and sales growth across the US.
New York City — Yuki, an emerging leader in data cost optimization, has closed a $6 million seed funding round. This substantial investment, led by Hyperwise Ventures, signals growing urgency for managing complex data infrastructure in the age of artificial intelligence. The funding round also saw participation from VelocitX, Tal Ventures, Fresh.fund, and Yakir Daniel, a co-founder of Spot.io. Yuki plans to leverage these funds for operational expansion and further development efforts.
Organizations face unprecedented challenges managing data infrastructure. Consumption soars. Budgets fluctuate wildly. Traditional data platforms, built on a "one-size-fits-all" model, struggle under current demands. Workloads, priorities, and service level agreements (SLAs) constantly compete for limited compute resources. The rapid rise of AI workloads exacerbates this issue. Query volumes skyrocket. Data consumption becomes highly unpredictable. This creates enormous pressure on cloud budgets, often with no clear mechanism for governance.
The company's founders underscore a pivotal shift. AI has fundamentally altered operational rules. Workloads are now more dynamic. They are less predictable. Running them is significantly more expensive. Conventional, uniform system setups cannot react in real-time. This forces teams into a dilemma: over-provision resources or accept performance losses. Yuki aims to resolve this critical imbalance.
Yuki's platform directly addresses this control deficit. It provides real-time optimization for major data platforms. This includes Snowflake, Google BigQuery, and Iceberg-based data lakes. The system improves both cost and performance. It achieves this by optimizing execution based on actual system behavior. It intelligently manages workloads and priorities in real-time. Crucially, it distinguishes between high-priority business tasks and lower-priority internal processes. Each query routes dynamically to the most efficient compute resources available. This dramatically reduces data costs. It tackles environments where data volumes and compute consumption continue their relentless growth, largely propelled by increased AI adoption.
The company was founded in 2025 by Ido Arieli Noga, CEO, and Amir Peres, CTO. Yuki has operated in stealth mode for the past year. Now, with seed funding secured, it advances its vision. The goal is to build a comprehensive control layer for modern data platforms. The company emphasizes a common organizational oversight: data often remains the sole resource without true management. Organizations possess budgets, cloud infrastructure, and dedicated teams. Yet, the data itself lacks a robust control system.
Yuki introduces automation directly into workload execution. Its platform does not merely monitor or report. It operates within the execution path itself. The core model, named Yuki Fabric, continuously learns. It analyzes workload behavior. It assesses performance requirements. It identifies cost patterns. Then, it dynamically routes queries. It allocates resources. It adjusts execution behavior in real-time. This means optimization becomes automatic, not manual.
Customers are already seeing significant returns. Early Yuki adopters have reported average savings of 42.6% on their data infrastructure costs. Some large enterprises have saved millions of dollars. The impact extends beyond mere cost reduction. A cultural shift often accompanies adoption. Teams transition from focusing on warehouse sizes and tuning tricks. They instead concentrate on workloads and desired outcomes. As the system adapts automatically, engineers develop trust. They believe the system uses resources wisely. This frees them to focus on building products, not managing infrastructure.
The investment timing is strategic. The AI boom generated a workload boom. Data platforms became far more dynamic. They also became much more expensive. Manual tuning simply cannot scale. Companies increasingly adopt multi-engine environments. They mix Snowflake, Databricks, ClickHouse, and open-source tools. Open table formats like Iceberg are also gaining traction. Without a unified control system, this fragmentation creates inefficiencies. Duplicated pipelines and workloads become common.
Snowflake cost optimization frequently presents the clearest symptom of this lack of control. Warehouse sprawl and unpredictable compute usage drive monthly budgets higher. The company notes that often, the same workload runs twice. This occurs not by design, but from a missing control layer above the engines. Yuki sits above these engines. It enables teams to manage complexity without constant re-architecting.
The rise of Iceberg and other open formats also changes governance requirements. When storage and compute are decoupled, and multiple engines access shared data, governance cannot reside within a single platform. Permissions, ownership, and auditability must migrate to the data layer itself. Without this fundamental shift, organizations quickly lose control. Data access becomes inconsistent. No single source of truth exists. A real-time control layer becomes crucial. It ensures the right engine is used. It enforces policies. It prevents efficiency gains from compromising governance at scale.
A key design principle for Yuki is zero-change deployment. The platform requires no code or query modifications. Enterprises seek modernization. However, they face constant delivery pressure. Solutions demanding migrations or refactors often fail to achieve widespread adoption. Zero-change deployment removes friction. It allows teams to prove immediate value.
Yuki's business model aligns with this philosophy. Customers pay only a percentage of realized savings. This alters the risk profile completely. No upfront risk exists. Budget guesswork is eliminated. No long-term commitment is required before value is proven. If the platform does not generate savings, Yuki does not get paid. This model ensures clear long-term value and mutual incentive alignment.
Yuki employs 15 people. Its customer base includes cybersecurity firms like Tenable. Data-heavy media companies, such as Angel Studios, also leverage its platform. These industries often exhibit spiky, unpredictable workloads. A common mistake in these sectors is over-provisioning. Teams size systems for peak usage and maintain that level. This creates substantial waste during normal periods. It also forces teams into continuous manual tuning. Yuki’s automatic adaptation prevents permanent over-provisioning.
The $6 million funding will accelerate R&D expansion in Israel. It will also fuel sales growth across the United States. Over the next two years, control and expansion will remain paramount. Product development will focus on real-time, workload-aware control. This becomes even more critical with AI and multi-engine setups. Customers will expect automatic optimization across engines. Stronger governance at the data layer is anticipated. Decisions will increasingly rely on live workload behavior, not static rules.
The market trend points toward continued AI adoption. Rising data costs in the US will pressure teams to achieve more with less. Companies will seek systems that actively reduce waste without hindering delivery. Yuki positions itself as an essential control layer. It transcends traditional cloud management tools. It offers intelligent, automated solutions for how enterprise data platforms operate. This next phase of enterprise data infrastructure will be defined by real-time control and execution-level automation.
New York City — Yuki, an emerging leader in data cost optimization, has closed a $6 million seed funding round. This substantial investment, led by Hyperwise Ventures, signals growing urgency for managing complex data infrastructure in the age of artificial intelligence. The funding round also saw participation from VelocitX, Tal Ventures, Fresh.fund, and Yakir Daniel, a co-founder of Spot.io. Yuki plans to leverage these funds for operational expansion and further development efforts.
Organizations face unprecedented challenges managing data infrastructure. Consumption soars. Budgets fluctuate wildly. Traditional data platforms, built on a "one-size-fits-all" model, struggle under current demands. Workloads, priorities, and service level agreements (SLAs) constantly compete for limited compute resources. The rapid rise of AI workloads exacerbates this issue. Query volumes skyrocket. Data consumption becomes highly unpredictable. This creates enormous pressure on cloud budgets, often with no clear mechanism for governance.
The company's founders underscore a pivotal shift. AI has fundamentally altered operational rules. Workloads are now more dynamic. They are less predictable. Running them is significantly more expensive. Conventional, uniform system setups cannot react in real-time. This forces teams into a dilemma: over-provision resources or accept performance losses. Yuki aims to resolve this critical imbalance.
Yuki's platform directly addresses this control deficit. It provides real-time optimization for major data platforms. This includes Snowflake, Google BigQuery, and Iceberg-based data lakes. The system improves both cost and performance. It achieves this by optimizing execution based on actual system behavior. It intelligently manages workloads and priorities in real-time. Crucially, it distinguishes between high-priority business tasks and lower-priority internal processes. Each query routes dynamically to the most efficient compute resources available. This dramatically reduces data costs. It tackles environments where data volumes and compute consumption continue their relentless growth, largely propelled by increased AI adoption.
The company was founded in 2025 by Ido Arieli Noga, CEO, and Amir Peres, CTO. Yuki has operated in stealth mode for the past year. Now, with seed funding secured, it advances its vision. The goal is to build a comprehensive control layer for modern data platforms. The company emphasizes a common organizational oversight: data often remains the sole resource without true management. Organizations possess budgets, cloud infrastructure, and dedicated teams. Yet, the data itself lacks a robust control system.
Yuki introduces automation directly into workload execution. Its platform does not merely monitor or report. It operates within the execution path itself. The core model, named Yuki Fabric, continuously learns. It analyzes workload behavior. It assesses performance requirements. It identifies cost patterns. Then, it dynamically routes queries. It allocates resources. It adjusts execution behavior in real-time. This means optimization becomes automatic, not manual.
Customers are already seeing significant returns. Early Yuki adopters have reported average savings of 42.6% on their data infrastructure costs. Some large enterprises have saved millions of dollars. The impact extends beyond mere cost reduction. A cultural shift often accompanies adoption. Teams transition from focusing on warehouse sizes and tuning tricks. They instead concentrate on workloads and desired outcomes. As the system adapts automatically, engineers develop trust. They believe the system uses resources wisely. This frees them to focus on building products, not managing infrastructure.
The investment timing is strategic. The AI boom generated a workload boom. Data platforms became far more dynamic. They also became much more expensive. Manual tuning simply cannot scale. Companies increasingly adopt multi-engine environments. They mix Snowflake, Databricks, ClickHouse, and open-source tools. Open table formats like Iceberg are also gaining traction. Without a unified control system, this fragmentation creates inefficiencies. Duplicated pipelines and workloads become common.
Snowflake cost optimization frequently presents the clearest symptom of this lack of control. Warehouse sprawl and unpredictable compute usage drive monthly budgets higher. The company notes that often, the same workload runs twice. This occurs not by design, but from a missing control layer above the engines. Yuki sits above these engines. It enables teams to manage complexity without constant re-architecting.
The rise of Iceberg and other open formats also changes governance requirements. When storage and compute are decoupled, and multiple engines access shared data, governance cannot reside within a single platform. Permissions, ownership, and auditability must migrate to the data layer itself. Without this fundamental shift, organizations quickly lose control. Data access becomes inconsistent. No single source of truth exists. A real-time control layer becomes crucial. It ensures the right engine is used. It enforces policies. It prevents efficiency gains from compromising governance at scale.
A key design principle for Yuki is zero-change deployment. The platform requires no code or query modifications. Enterprises seek modernization. However, they face constant delivery pressure. Solutions demanding migrations or refactors often fail to achieve widespread adoption. Zero-change deployment removes friction. It allows teams to prove immediate value.
Yuki's business model aligns with this philosophy. Customers pay only a percentage of realized savings. This alters the risk profile completely. No upfront risk exists. Budget guesswork is eliminated. No long-term commitment is required before value is proven. If the platform does not generate savings, Yuki does not get paid. This model ensures clear long-term value and mutual incentive alignment.
Yuki employs 15 people. Its customer base includes cybersecurity firms like Tenable. Data-heavy media companies, such as Angel Studios, also leverage its platform. These industries often exhibit spiky, unpredictable workloads. A common mistake in these sectors is over-provisioning. Teams size systems for peak usage and maintain that level. This creates substantial waste during normal periods. It also forces teams into continuous manual tuning. Yuki’s automatic adaptation prevents permanent over-provisioning.
The $6 million funding will accelerate R&D expansion in Israel. It will also fuel sales growth across the United States. Over the next two years, control and expansion will remain paramount. Product development will focus on real-time, workload-aware control. This becomes even more critical with AI and multi-engine setups. Customers will expect automatic optimization across engines. Stronger governance at the data layer is anticipated. Decisions will increasingly rely on live workload behavior, not static rules.
The market trend points toward continued AI adoption. Rising data costs in the US will pressure teams to achieve more with less. Companies will seek systems that actively reduce waste without hindering delivery. Yuki positions itself as an essential control layer. It transcends traditional cloud management tools. It offers intelligent, automated solutions for how enterprise data platforms operate. This next phase of enterprise data infrastructure will be defined by real-time control and execution-level automation.