Zro

Zro is a platform designed to provide developers with fast and optimized inference capabilities for open-weight AI models, specifically targeting coding agents. It aims to bridge the gap between using closed-source AI APIs, which often compromise privacy and control, and self-hosting open-weight models, which requires significant infrastructure management. The core problem Zro addresses is the difficult trade-off developers face when building AI products. Many developers need the privacy and control offered by open-weight models but are deterred by the complexity and cost of managing their own inference infrastructure. Zro eliminates this dilemma by offering a managed solution that prioritizes speed, efficiency, and data privacy. Key features of Zro include multi-region hosted inference, ensuring low latency and high availability across different geographical locations. A critical aspect is its zero data retention policy, meaning no prompts or completion content are stored, and no data is used for training, which is paramount for sensitive code-related tasks. For organizations with stricter requirements, Zro also offers optional on-premise deployments, providing maximum control over data and infrastructure. The platform boasts a serving stack specifically optimized for coding agents and long-context workloads. This optimization is powered by MoonMath's in-house inference technology, designed to enhance performance for complex tasks. Zro also provides an OpenAI-compatible API, making integration seamless for developers already familiar with OpenAI's ecosystem. This compatibility significantly reduces the effort required to switch from OpenAI to Zro, as existing agent orchestration layers and API schemas can often be used with minimal modification. Zro supports a range of popular AI coding agents and harnesses, including Claude Code, Codex, Pi, and OpenCode, with ongoing efforts to add more, such as Kilo Code and Grok Build. The platform is built for easy setup, with integrations readily available to streamline the adoption process for developers. The team is actively seeking feedback on which models to add next and what features are most desired by users. Zro operates on a methodology that prioritizes both performance and privacy. By optimizing its inference stack for specific use cases like coding agents and long-context tasks, it aims to deliver low latency and high token-per-second rates without compromising its zero-retention guarantee. The multi-region infrastructure ensures that users can select a region close to them for optimal performance, and the optional on-premise deployment offers an additional layer of control. The benefits for users include enhanced privacy and control over their code and data, faster inference speeds optimized for coding tasks, and simplified infrastructure management. Developers can leverage powerful open-weight models without the operational burden of self-hosting, leading to more efficient development cycles and greater peace of mind regarding data security. Concrete use cases for Zro include developing AI-powered code generation tools, building intelligent code completion assistants, creating automated code review systems, and deploying private AI agents for sensitive code analysis. Any application that requires processing proprietary code or sensitive information can benefit from Zro's privacy-focused inference. Zro is primarily targeted at developers and teams building AI products, particularly those working with coding agents or handling sensitive code. The platform is web-based and offers an API. While specific pricing tiers are not detailed, a launch offer provides the first month of Zro Pro free with a specific code. The team is also exploring TEE-based verification for enhanced transparency. In summary, Zro empowers developers by providing a fast, private, and efficient inference solution for open-weight models, specifically tailored for the demands of coding agents and long-context workloads, thereby removing the traditional trade-offs between privacy and performance.