OpenClaw Gateway
Self-hosted AI gateway that routes between local and cloud LLMs — keeping sensitive data on-premise while accessing frontier models when needed
Most businesses face a choice: send everything to cloud AI providers and accept the data exposure risk, or run local models and accept lower quality. Neither option works well for organisations handling sensitive client data — law firms, financial advisors, healthcare providers, or any business subject to GDPR.
What OpenClaw does
OpenClaw is a self-hosted gateway that sits between your applications and multiple AI providers. It makes intelligent routing decisions: sensitive queries stay on your local Ollama instance, while complex tasks that need frontier reasoning get sent to Claude or GPT — with PII automatically stripped before leaving your network.
The gateway provides a single API endpoint. Your applications do not need to know which model is handling their request. They send a prompt, and OpenClaw routes it based on rules you define: data sensitivity, task complexity, cost constraints, and latency requirements.
How businesses use it
Financial services: Client portfolio queries stay local. Market analysis goes to Claude. No client names or account numbers ever leave the building.
Legal firms: Contract review and legal research uses frontier models. But case details, client names, and privileged information are processed entirely on-premise.
Healthcare: Patient data stays within NHS-compliant infrastructure. General medical knowledge queries use cloud models for better accuracy.
Consulting: Internal strategy documents processed locally. External market research and analysis leverages cloud models for depth.
Technical approach
The gateway runs as a lightweight Node.js service. It maintains connections to local Ollama instances and cloud APIs simultaneously. A rule engine evaluates each request against configurable policies — checking for PII patterns, data classification tags, and content sensitivity markers before deciding where to route.
Cost tracking is built in. Every request logs the model used, token count, latency, and estimated cost. Monthly reports show exactly how much you are spending on cloud vs local inference.
Where it performs well
- Businesses handling regulated data (financial, legal, healthcare)
- Companies with GDPR obligations who need AI capabilities
- Organisations wanting to reduce cloud AI spend by routing simple tasks locally
- Teams that need consistent API access regardless of which model handles the work