Leil raises €1.5 million to cut data storage energy use
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Leil raises €1.5 million to cut data storage energy use

Estonian startup makes hyperscale SMR storage cheaper and greener

11/6/2025
Ali Abounasr El Alaoui
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Tallinn-based Leil has raised €1.5 million in seed funding to push hyperscale storage economics into the mainstream. The round, announced on November 6, 2025, was led by Karma Ventures with participation from Specialist VC. The company develops data storage-infrastructure software that lowers cost and energy use for organizations managing enormous datasets.


Market Context

Enterprise storage is a roughly $22 billion market and is expanding at close to 20 percent annually. Global data creation exceeds 330 million terabytes per day, with volumes expected to triple by 2030. At today’s pace, the world generates a full year of 2010’s data in about six days, creating an acute need for efficient, lower-energy storage.

Technology and Product

Leil’s platform makes high-density shingled magnetic recording hard drives viable at enterprise scale. SMR overlaps tracks like roof shingles to pack about 20 percent more data per disk than conventional CMR drives used in most data centers. That density translates into roughly 20 percent lower cost per terabyte and reduced power consumption, compounding savings across hardware, cooling, and operations.

Overcoming SMR Complexity

The catch with SMR is that data must be written sequentially to avoid overwrites or corruption, which historically demanded bespoke tooling. For more than a decade, practical SMR deployment remained the domain of hyperscalers such as Google, AWS, and Meta, who built proprietary systems but did not commercialize them. Leil abstracts that complexity, orchestrating how data is written, moved, and recovered across thousands of drives while preserving higher density and lower energy use.

Intelligent Power Management

Leil’s software analyzes access patterns, groups cold content, and powers down the corresponding drives until the data is needed again. Depending on workload, the approach enables energy savings of up to 70 percent without sacrificing availability. The design is HDD-native, aiming to deliver a resilient, high-performance foundation for long-term data growth.

Early Adoption and Use Cases

SMR is especially relevant for national broadcasters, public archives, AI training centers, and research institutions that manage multi-petabyte installations. Leil reports customers across these segments, indicating traction beyond early pilots. Industry examples show that large fleets can benefit from SMR economics, with operators reporting per-terabyte gains in cost and power efficiency.

Business Model and Deployment

Leil sells on a capacity-based subscription that aligns pricing with retained data volumes. The platform supports gradual transitions, allowing enterprises to run alongside existing hardware and integrate SMR drives stepwise as refresh cycles permit. New funding will accelerate go-to-market execution, expand the product roadmap, and scale the commercial team.

Executive and Investor Perspectives

Co-founder and CEO Aleksandr Ragel said the cost and energy burden of data is hindering progress in AI and science, and the company aims to “make hyperscale storage economics a reality for every enterprise.” He emphasized cost savings, environmental impact, and data sovereignty as core benefits of Leil’s HDD-native approach. Karma Ventures partner Kristjan Laanemaa said the team combined deep infrastructure expertise with productization skill, adding that AI requires “smarter, sovereign infrastructure” for the datasets models rely on.


Leil is targeting a clear bottleneck by operationalizing SMR at scale for organizations outside the hyperscaler elite. If its software continues to unlock higher density and automated power management, enterprises could materially reduce storage costs and energy footprints. With fresh capital and a capacity-aligned model, the company is positioned to court data-intensive sectors that need hyperscale economics without hyperscale resources.