This isn't "I replaced ChatGPT with local models." It's "I built an AI platform where cloud and local models each do what they do best."

Opening

In 2026, almost every AI tutorial starts with a cloud API key.

OpenAI. Claude. Gemini.

They're incredible services, and I use them every day.

But after months of building AI applications for clients, I found myself asking a different question:

What if AI wasn't a service I consumed?

What if it became infrastructure I owned?

That question led me down a rabbit hole that transformed a couple of spare computers, a Raspberry Pi, and my home network into a distributed AI engineering platform.

Today my home lab runs local language models, vector databases, AI gateways, monitoring, CI/CD pipelines, remote access, and production applications that I use every day.

This series is the story of how I built it.

The Problem

Cloud AI is fantastic.

But I kept running into the same problems:

  • Paying for inference that didn't need cloud-scale models.
  • Context switching between different AI providers.
  • Different APIs and authentication methods.
  • Duplicate model downloads across machines.
  • Development environments that depended on an internet connection.
  • Wanting to experiment without worrying about token costs.

None of these were major issues individually.

Together, they pushed me toward building something different.

The Goal

The objective was never to eliminate the cloud.

Instead, I wanted an environment where:

  • Local models handled routine engineering work.
  • Premium cloud models were used when they genuinely added value.
  • Every IDE could access every model through a single endpoint.
  • Models lived in one place instead of being duplicated.
  • AI became part of my infrastructure rather than another SaaS subscription.

What Exists Today

Without spending enterprise money, my home lab now includes:

  • Ubuntu servers running Docker
  • NVIDIA GPU inference
  • Raspberry Pi infrastructure
  • Local LLMs
  • Qdrant vector database
  • AI gateway routing requests across providers
  • Cloudflare Tunnel
  • CI/CD
  • Monitoring
  • Production applications

Most importantly:

I don't think about which machine or model I'm using anymore.

I simply ask the question, and the infrastructure handles the rest.

Who This Series Is For

This series isn't aimed at people trying to build a ChatGPT clone.

It's for software engineers, architects, founders, and AI builders who want to create a development environment that is:

  • faster,
  • cheaper,
  • more private,
  • and completely under their control.

I'll cover the successes, the failures, the hardware I regret buying, the software I replaced, and the architecture that finally worked.

Building an AI Home Lab

Next, in Part 2, I'll explain how I manage multiple local models as scheduled workloads through a single endpoint.