HOAI: Technical Architecture Analysis of an Open-Source Full-Scenario AI Service and Operation Platform

HOAI is an open-source AI service platform developed with Node.js full-stack technology. Positioned as a full-scenario solution, it supports multi-user, multi-model, and multi-modal conversations, with a built-in management backend, payment system, and risk control capabilities. It aims to solve issues such as data privacy, cost control, and limited customization in enterprise private AI deployment, making it suitable for quickly building private AI services.

With the evolution of large language model capabilities, enterprises and developers have an increasing demand for private AI deployment. Although public APIs are convenient, they have issues like data privacy, cost control, and limited customization. A complete private AI platform needs to address problems such as multi-model access management, user permission control, billing and payment, and content security risk control. HOAI is exactly a full-scenario solution designed for these needs.

HOAI (HubOpenAI) is an open-source full-scenario AI service and operation platform built with Node.js full-stack technology. It is positioned as a ready-to-use complete solution for self-built AI service teams (not a simple API proxy). Core functions include:

• Conversation and multi-modality: Supports text/image/file interaction, connects to multiple model backends and automatically handles differences;
• Management backend: User management, model configuration, usage monitoring, content auditing;
• Payment and billing: Multiple billing models, financial processes, payment gateway integration;
• Risk control and security: Input filtering, output review, abnormal behavior detection.

HOAI uses a Node.js full-stack architecture:

• Backend tech stack: Node.js + Express/Fastify (asynchronous IO for high concurrency); Database layer uses PostgreSQL/MySQL (structured data), Redis (caching/rate limiting), optional vector database (RAG); Model access layer supports OpenAI-compatible APIs, Anthropic, local models (llama.cpp/vLLM), and custom models via adapter pattern;
• Deployment and operation: Docker containerization (one-click dependency startup), PM2 process management (multi-process/automatic restart/logs), one-click build script (fast deployment).

Applicable scenarios:

• Private AI services for small and medium enterprises (intranet deployment, data control);
• MVP validation for AI startups (quick prototype building, shorter time to launch);
• Education and training institutions (customized platform, usage monitoring);
• Developers' personal projects (free modification and secondary development). Comparison: Compared to reverse proxy tools like One API, HOAI provides full-scenario operation capabilities; compared to commercial solutions, its open-source nature offers lower costs and higher customization freedom.

Deployment recommendations:

• Hardware requirements: Minimum 2 cores and 4GB RAM; production environment requires 4 cores and 8GB RAM or more; local models need additional GPU;
• Security hardening: Enable HTTPS, change default passwords, rotate API keys, enable log auditing;
• Model quota management: Set reasonable quotas for different user groups to prevent resource exhaustion or high costs.

HOAI represents the maturation of open-source AI infrastructure, providing complete operation support capabilities and balancing functional integrity with deployment complexity. It is a worthy option for self-built AI service teams to evaluate. With the evolution of multi-modal models and Agent technology, such full-scenario platforms will play a more important role as infrastructure.