From Direct Classification to Agentic Routing: Local vs Cloud AI

You're building AI workflows. Support tickets, document parsing, query routing. The question hits immediately: local or cloud? Most people pick one. Local for privacy, cloud for power. But that's leaving performance and cost on the table. I've been building MCP servers for SeaynicNet. Some queries need GPT-4's reasoning. Others are trivial pattern matching that a local model handles in milliseconds. Routing everything to GPT-4 works, but it's expensive overkill. The real answer? Agentic routing. Let a lightweight local model triage requests. Simple ones stay local. Complex ones go to the cloud. You get speed, cost efficiency, and power when you actually need it. I'll walk through the architecture, cost analysis, and code samples. This isn't theory—it's running in production.