Zro

Product Introduction

1. Definition: Zro is a specialized, multi-region inference endpoint service designed for AI-powered coding agents and developer tools. It falls under the technical categories of Machine Learning as a Service (MLaaS) and AI inference infrastructure.
2. Core Value Proposition: Zro exists to provide developers and enterprises with fast, private, and optimized inference for open-weight coding models. Its primary value is delivering zero request retention and no data training by default, combined with low-latency performance across global regions, specifically tuned for long-context, multi-turn coding sessions.

Main Features

1. Private, Multi-Region Inference Infrastructure: Zro operates on a privacy-forward infrastructure distributed across multiple global regions. This ensures low-latency access for users worldwide while enforcing a strict zero data retention policy. Customer prompts and completions are not stored after processing and are never used for model training, fine-tuning, or analytics.
2. Optimized Performance for Coding Workloads: The service is not a generic model host; it is specifically engineered for coding-agent workloads. Underneath its API, MoonMath (the developer) applies HyperQuant compression, custom attention kernels, and hardware-aware deployment on AMD, NVIDIA, and TPU hardware. This technical stack is designed to handle long-context prompts typical in coding sessions with responsive, streaming inference.
3. Unified Endpoint for Open Coding Models: Zro provides a single, consistent API endpoint for multiple state-of-the-art open coding models. It currently serves MiniMax M3, GLM-5.2 from Z.ai, and Kimi K2.7 Code from Moonshot AI, with plans to add more like DeepSeek V4 Pro. This eliminates the need for developers to manage separate integrations and infrastructure for each model.
4. Seamless Developer Tool Integration: Zro offers multiple integration paths. It provides an official @moonmath-ai/zro npm package for CLI-based launching of supported tools like Claude Code and Codex CLI. Furthermore, it exposes both OpenAI-compatible (/v1/chat/completions) and Anthropic-compatible (/v1/messages) API endpoints, allowing existing AI agent frameworks, IDEs (like Cursor and Cline), and custom applications to connect with minimal configuration changes.

Problems Solved

1. Pain Point: Developers and companies using AI coding assistants face a trade-off between performance, privacy, and model choice. Closed-source providers (e.g., OpenAI, Anthropic) may retain or train on user data, while self-hosting open models requires significant infrastructure expertise and cost to achieve low latency.
2. Target Audience: The primary user personas are Software Developers, DevOps Engineers, and Engineering Managers who utilize AI coding assistants (Claude Code, Cursor, Cline, etc.) daily and are concerned about code privacy and intellectual property. Secondary users include Startups and Enterprises building AI-powered developer tools or internal coding co-pilots who need a reliable, scalable, and private inference backend.
3. Use Cases:
   • Private Code Generation: A developer working on proprietary algorithms or unreleased product code can use their preferred AI coding assistant via Zro without fear of prompt or completion data being retained or used to improve public models.
   • Low-Latency Agent Backend: A company building an internal AI agent for code review or refactoring can use Zro's multi-region endpoints to ensure fast response times for their global engineering team.
   • Unified Model API for Development Tools: A tool like Cursor or an open-source agent framework can be configured to use Zro's endpoint to access multiple cutting-edge open coding models through a single, standardized API, simplifying maintenance and offering model choice to end-users.

Unique Advantages

1. Differentiation: Unlike generic cloud AI services or model hubs, Zro is hyper-specialized for coding tasks. Compared to self-hosting solutions, it removes the operational burden of deployment, optimization, and global scaling. Versus other inference providers, its strict default policy of zero retention and no training is a core, non-negotiable feature rather than an opt-in or enterprise-only offering.
2. Key Innovation: The combination of HyperQuant compression and custom kernels applied specifically to open coding models is a significant technical innovation. This allows Zro to deliver inference speeds that users describe as "the fastest API I have ever used" while maintaining model quality, which is critical for the interactive, real-time experience required by coding agents.

Frequently Asked Questions (FAQ)

1. Does Zro store or use my code prompts and completions for training? No. Zro operates with zero request retention by default. Your prompt and completion data are not stored after inference is complete and are never used for training, fine-tuning, evaluations, or dataset creation.
2. How do I connect my existing AI coding assistant (like Cursor or Cline) to Zro? You can connect most tools by setting their API base URL to Zro's endpoint and using your Zro API key. Detailed manual setup guides for popular IDEs are provided. For supported CLI tools like Claude Code, you can use the zro launch command from the official npm package for automatic configuration.
3. What coding models are available on the Zro inference platform? Zro currently provides access to the MiniMax M3, GLM-5.2, and Kimi K2.7 Code open-weight models for coding tasks. Availability per region is shown when you create an API key. The platform is built to easily integrate new, competitive open coding models as they are released.
4. Is the Zro API compatible with libraries built for OpenAI or Anthropic? Yes. Zro provides full OpenAI-compatible chat completions and Anthropic-compatible Messages API endpoints. This means most existing AI SDKs, agent frameworks, and applications designed for these APIs can switch to Zro by simply changing the base URL and API key.
5. How is Zro's performance optimized specifically for coding agents? Zro is tuned for long-context, multi-turn sessions typical in coding. It uses HyperQuant compression to reduce model size with minimal quality loss, custom attention kernels for efficient processing of long sequences, and hardware-aware deployment across optimized GPU/TPU infrastructure to deliver the low-latency, streaming responses essential for developer productivity.