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

Ora Computing, a startup developing software to optimise and compress AI foundation models, has closed a €3.5 million seed funding round led by Constructor Capital and Greencode Ventures, with continued support from founding investor XISTA Science Ventures. As AI adoption accelerates, the cost of AI inference has become one of the industry’s most significant challenges. Organisations deploying AI at scale increasingly face compute costs reaching tens of millions of euros per month, while the growing size of foundation models creates additional barriers for applications that require local deployment on devices such as vehicles, industrial equipment, and edge hardware. Ora Computing addresses this challenge through software that compresses AI models by up to 80 per cent, enabling them to run up to four times faster while maintaining high performance, with accuracy reductions typically ranging between 0 and 5 per cent. By reducing the computational resources required for inference, the technology also lowers energy consumption and associated carbon emissions. The company estimates that achieving just 1 per cent market penetration could result in annual CO₂ savings exceeding 50,000 tonnes. Stefan Sack, CEO and co-founder of Ora Computing, said the company was created to rethink the conventional view that larger models are the only path to achieving practical intelligence: We believe the next wave of AI adoption will be driven by compact, highly efficient models optimised for specific applications rather than increasingly large, general-purpose cloud models. Ora is building the software and algorithmic foundation that enables this transition. Unlike many existing model compression approaches, Ora’s technology operates across different hardware platforms and integrates directly with standard inference frameworks, eliminating the need for custom software layers, infrastructure changes, or capital-intensive retraining. The company’s algorithms continuously map the trade-off between model size and accuracy, allowing customers to optimise deployments based on their specific hardware, performance, and cost requirements. Ora has demonstrated this capability by compressing a 70-billion-parameter model within hours at a compute cost of less than $1,000, compared with industry benchmarks that can reach hundreds of thousands of dollars for similar tasks. The newly secured funding will support team expansion, further development of the company’s compression capabilities for the largest frontier models, and the launch of a commercial product targeting cloud inference providers and organisations deploying AI at the edge.