Thoras.ai Secures $1.5M to Boost Enterprise Cloud Optimization and Resilience

By Stefania Viscusi  |  March 28, 2024

Thoras.ai announced the successful closure of a pre-seed funding round of $1.5 million. The investment, co-led by Storytime Capital and Focal VC, with additional contributions from Hustle Fund, Precursor Ventures, the Pitch Fund, and strategic angel investors, will propel the company’s platform development focused on optimizing and fortifying enterprise cloud computing solutions.

Founders Nilo Rahmani and Jen Rahmani pooled their diverse expertise in reliability engineering and machine learning to develop a solution that addresses the challenges confronting enterprises when it comes to effectively scaling their cloud workloads and monitoring services.

Thoras.ai offers AI-driven scaling and anomaly detection capabilities for cloud workloads. Its goal is to elevate cloud service reliability and efficiency by employing predictive algorithms and delivering continuous optimization at scale. By autonomously scaling resources in anticipation of demand and proactively monitoring and alerting users, it minimizes unpredictability and enhances reliability for enterprise clientele.

One of Thoras.ai's standout features is its utilization of machine learning to forecast the needs of cloud workloads. It offers actionable recommendations to users to optimize services and avoid system failures. The proactive approach to scaling and monitoring workloads has demonstrated tangible benefits, and Thoras.ai boasts a reduction in cloud costs by up to 50% compared to static policies, while simultaneously bolstering system uptime.

"We believe Site Reliability Engineering is an art, and every artist requires a quality toolkit. With the support of our investors, we're excited to embark on our mission to transform infrastructure management with proactive decision-making capabilities and reduced dependence on overprovisioning,” said Nilo Rahmani, CEO of Thoras.ai.

"This funding will enable us to accelerate the development of our platform, bringing predictive insights and optimization to container-based environments, and ultimately helping our customers save both time and money."




Edited by Greg Tavarez
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