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OpenClaw On-Premises Deployment Solution

Two deployment paths for the self-hosted OpenClaw AI assistant: Solution A - local GPU private deployment (free, secure, reuse) and Solution B - cloud API quick access (fast, low-barrier, advanced). Built for schools, institutions, and enterprise AI rollouts.

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Solution architecture diagram
OpenClaw On-Premises Deployment Solution Solution architecture diagram
Solution contents
00

Overview

Deliverables

OpenClaw On-Premises Deployment Solution

1OpenClaw self-hosted AI assistant platform
2Solution A local GPU deployment
3Solution B cloud API integration
4Selection guidance and O&M support
5Unified Gateway and CLI tooling
5
Deliverables
6
delivery chapters
01

OpenClaw Platform Architecture & Multi-Channel Access

A self-hosted AI assistant supporting WhatsApp, Telegram, Slack, Discord, and WeChat - compatible with both local and cloud models.

Capability points
1自托管个人AI助手平台
2支持WhatsApp/Telegram/Slack/Discord/微信等多渠道接入
3兼容本地开源模型（Ollama、vLLM）与云端API（Anthropic、OpenAI、通义千问等）
4统一Gateway服务与CLI工具
5可扩展RAG知识库与业务工具调用
OpenClaw Platform Architecture & Multi-Channel Access Interface preview
02

Solution A: Local GPU Private Deployment

01

Ollama本地推理服务集成（端口127.0.0.1:11434）

02

5步标准化部署流程（Ollama安装→OpenClaw安装→Onboarding→Gateway→验证）

03

支持Qwen3.5:27b / Llama3.3 / GLM-4.7-flash等开源模型

04

交互式与非交互式两种Onboarding配置

05

systemd Gateway守护进程与loginctl enable-linger持久化

Reuse existing GPU + Ollama inference, 5-step standardized deployment, and data that never leaves the campus network.

Solution A: Local GPU Private Deployment Interface preview
03

Solution B: Cloud API Quick Access

One-line install against 7+ cloud providers, unified through OpenRouter, with a monthly cost of RMB 100-1,000.

  • 一键脚本安装OpenClaw（curl -fsSL https://openclaw.ai/install.sh | sh）
  • 支持7+家云端Provider（Anthropic/OpenAI/通义千问/Kimi/MiniMax/Mistral/OpenRouter）
  • OpenRouter统一聚合入口
  • config.yaml模型配置管理
  • 月均成本100-1000元的轻量级部署
Solution B: Cloud API Quick Access Interface preview
04

Deployment Selection Decision Framework

Four decision factors (compute, privacy, complexity, O&M) combined with a GPU and intranet dual-track decision flow.

  • 4项关键决策因素评估（算力/隐私/复杂度/运维）
  • GPU可用性与数据合规双重决策流程
  • 场景化建议（学校机构/个人轻量/混合场景）
  • 已有GPU资源复用策略（内网调用Ollama服务）
  • 企业vs个人两条核心决策原则
Deployment Selection Decision Framework Interface preview
05

Operations Commands & Environment Variables

8 core ops commands and 5 environment variables, with `doctor` auto-diagnosis and PATH troubleshooting built in.

01

8个核心运维命令（status/doctor/logs/dashboard/restart/stop/secrets reload等）

Core
02

5个核心环境变量（OPENCLAW_HOME/STATE_DIR/CONFIG_PATH/GATEWAY_PORT/GATEWAY_TOKEN）

03

深度健康检查（gateway status --deep）

04

openclaw doctor自动诊断与修复

Implementation flow
Operations Commands & Environment Variables Interface preview
06

School/Institution Private Deployment Value

Capability points

01
数据安全与合规（学生/科研数据不出校园网）
02
成本可控（复用已有GPU服务器，避免长期Token消耗）
03
统一入口（多部门/实验室权限、模型、API密钥集中管理）
04
体验一致（用户无感知的底层模型切换）
05
可扩展能力（后续可对接本地知识库RAG与业务工具）

Data stays on the campus network, GPU is reused, and models/API keys are managed centrally - with headroom for RAG and business tool integration later.

School/Institution Private Deployment Value Interface preview
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Capability points
06
Implementation analysis
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