Self-Hosted AI vs. Cloud AI: A Practical Comparison for Developers

You're building something with AI. Now you need to decide: do you spin up your own infrastructure and self-host, or do you hand the keys to a cloud AI provider and pay per token? It's one of the most common architectural decisions developers face right now, and both paths come with real trade-offs. This post breaks it down practically — no hype, just the stuff that actually matters when you're shipping. What We Mean by "Self-Hosted" vs. "Cloud AI" Before diving in, let's align on definitions. Cloud AI means using a managed AI service — think OpenAI's API, Google Vertex AI, AWS Bedrock, or Azure OpenAI. You send a request, the provider runs the model on their infrastructure, and you get a response back. You never touch a server. Self-hosted AI means you're running the model (or AI agent/tool) yourself — on your own VPS, on-prem hardware, or a rented bare metal server. Tools like n8n, Dify, Langflow, Open WebUI, and Flowise fall into this category. You control the stack. Round 1: Cost Cl