A subscription-free, privacy-first AI infrastructure stack using locally-run large language models, custom tool-calling pipelines, and persistent memory services — all orchestrated without a single paid API call.
This infrastructure project explores how to build production-grade AI agent capabilities without relying on paid cloud APIs. Using Ollama to run models locally, n8n to orchestrate multi-step workflows, LangChain for tool-calling and chain management, and Google ADK for extended agent capabilities — the result is a fully self-hosted AI stack that powers real use cases including data analysis, document Q&A, and custom automation pipelines.
Running LLMs locally addresses three critical concerns: cost (no per-token billing), privacy (data never leaves the machine), and reliability (no dependency on third-party uptime). This stack proved that local models, when properly orchestrated, can match cloud API performance for a wide range of agentic tasks — making it a viable foundation for production AI features in privacy-sensitive or cost-constrained environments.