Ora Computing raises €3.5M seed round to build the efficiency layer of the AI stack

The Austrian startup’s software shrinks AI models by up to 80%, letting companies run more AI on less hardware. Ora Computing, a startup specializing in optimizing and compressing AI foundation models, today announced the close of a €3.5 million seed round led by Constructor Capital and Greencode Ventures, with continued backing from foundational investor XISTA Science Ventures, who helped build and launch the company.

AI inference – the process of actually running an AI model to generate outputs – has become a significant and fast-growing cost for any company deploying AI at scale. Major deployments can now cost tens of millions of euros per month in compute alone, and the problem compounds as models continue to grow in size. For companies wanting to run AI locally on devices like cars or industrial equipment, the models are often simply too large to fit.

Ora’s software compresses those models – shrinking their size by up to 80% and making them run up to four times faster – while keeping accuracy loss between 0 and 5%. Because compressed models require significantly less compute power to run, the efficiency gains also translate directly into lower energy consumption and reduced carbon emissions: at 1% market penetration, Ora estimates its technology could eliminate more than 50,000 tonnes of CO₂ annually.

“We founded Ora Computing to challenge the assumption that massive scale is needed to reach useful intelligence. We believe that the next wave of AI adoption will be driven by more compact models that are highly efficient and optimized for specific use cases rather than large general purpose cloud models. Ora is building the software and algorithm stack that enables this transition.” says Stefan Sack, CEO & co-founder of Ora Computing.

Unlike existing compression tools, Ora’s approach works across different hardware types and drops directly into standard inference frameworks – no custom software layers, no capital-intensive retraining, no changes to existing infrastructure. Where competing approaches force a binary choice between compression levels, Ora’s algorithm continuously maps the full tradeoff between model size and accuracy, letting companies optimize for their specific hardware and cost constraints. Ora has proven this with a 70 billion parameter model compressed in hours at a compute cost of under $1000, compared to industry figures of hundreds of thousands of dollars for comparable work.

Ora was founded by Stefan Sack and Raimel Medina, both quantum computing researchers from the Serbyn group at the Institute of Science and Technology Austria (ISTA), one of Europe’s leading research institutions. The company emerged from stealth in late 2025 and has since validated its compression solution with players in the Automotive and Edge Silcon sectors. The fresh funding will be used to grow the team, extend compression capabilities to the largest frontier models, and launch a commercial product for cloud inference providers and companies deploying AI at the edge.

“AI’s energy appetite is growing faster than the world can build the infrastructure to feed it. One key approach is to make AI itself more efficient, and that is exactly what Ora does. Compressing models radically without sacrificing accuracy makes a tremendous difference to their customers,” says Terhi Vapola, Founder and Managing Partner of Greencode Ventures.

***

 For additional information:

Stefan Sack, CEO & co-founder
Ora Computing
stefan@oracomputing.com