Zerops raises $2M seed to rebuild cloud infrastructure for the AI development era

· Source: Tech.eu - Tech.eu · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, short

Summary

Zerops, a Platform-as-a-Service (PaaS) startup, has secured a $2 million seed round led by Gi21 Capital to advance its cloud infrastructure designed for the AI development era. The platform eliminates the traditional divide between development and production environments by providing a unified project space where applications behave identically regardless of scale. Built on bare-metal infrastructure with data centers in Europe and the US, Zerops offers cost efficiencies up to four times cheaper than legacy platforms. It utilizes full Linux containers, granting developers extensive access and control, and includes over 15 built-in services. Zerops also introduces the Zerops Control Panel (ZCP), enabling AI coding agents like Claude, Codex, or Gemini to build, deploy, and debug applications directly within real cloud infrastructure. The funding will support global infrastructure expansion, product development, and team growth.

Key takeaway

For CTOs and VPs of Engineering evaluating cloud infrastructure, Zerops presents a compelling alternative to traditional PaaS offerings. Its unified environment and bare-metal architecture promise significant cost savings and eliminate common deployment failures, making your development cycles more predictable. Consider piloting Zerops to streamline your CI/CD pipelines and enhance the productivity of both human and AI development teams.

Key insights

Zerops unifies dev and production environments, enabling reliable deployments and AI-driven development.

Principles

Method

Zerops creates a single, consistent environment for development and production, allowing applications to behave identically from day one, and integrates AI coding agents directly into real cloud infrastructure for building and debugging.

In practice

Topics

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Software Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Tech.eu - Tech.eu.