MoonMath AI Launches Zro: Private Inference Endpoint for Coding Agents with Zero Data Retention
Key Takeaways
- ▸Zro is a privacy-first inference endpoint for coding agents, running on EU infrastructure with zero data retention and zero training on customer data
- ▸Supports open-weight coding models including MiniMax M3, GLM-5.2, and upcoming options from DeepSeek and Moonshot AI
- ▸Seamlessly integrates with existing developer tools through OpenAI-compatible and Anthropic-compatible APIs, plus CLI tooling via npm
Summary
MoonMath AI has launched Zro, a private inference endpoint designed specifically for coding agents and developer tools. Built on EU infrastructure in regions including Finland and France, Zro guarantees zero request retention, zero training on customer data, and optimized performance for long-context, multi-turn coding sessions. The service supports open-weight models including MiniMax M3 and GLM-5.2, with additional models from DeepSeek and Moonshot AI coming soon.
Zro integrates seamlessly with existing development workflows through multiple approaches: developers can use the npm package (@moonmath-ai/zro) to launch supported coding tools like Claude Code, Codex CLI, OpenCode, Hermes, and OpenClaw with a single command. The service exposes both OpenAI-compatible and Anthropic-compatible APIs, allowing existing clients and agent tools to switch their inference endpoints to Zro without code changes. Manual setup is also available for tools like Cursor and Cline.
The service employs MoonMath's proprietary HyperQuant compression, custom kernels, and hardware-aware deployment to deliver responsive, streaming inference without compromising on privacy. Pricing starts at $20/month for $60 in inference spend, with usage packs available without subscription.
- Delivers streaming inference performance without trading speed for privacy, optimized for long-context coding workloads
- Pricing starts at $20/month with usage-based billing; EU regions include Finland and France
Editorial Opinion
Zro represents a critical shift in how coding agents can access model inference while maintaining strict privacy guarantees—addressing growing regulatory and ethical concerns around EU data protection. The support for open-weight models alongside compatibility with Anthropic's API signals confidence that open alternatives are becoming genuinely competitive for coding tasks. By making private inference accessible to developers at reasonable costs, MoonMath lowers barriers to privacy-conscious AI agent deployment, which could accelerate adoption in regulated industries.
