MixAI: I’m choosing a mix of local and cloud AI — here’s why

Almost a year of hands-on experiments. Hardware ranging from $500 to $15,000. Cloud APIs, AI agents, and real-world tasks for businesses and homes. From now on, I’ll be sharing more about this combined approach to AI — one I believe has a very promising future.

Things are changing almost every day. But AI technology has already reached a point where we can achieve reliable results and realistically plan how to use it over the next year or two.

And I’ve come to a simple conclusion: we don’t need to choose between local and cloud AI. We need to learn how to get the best out of both.

This goes far beyond simply connecting different models or orchestrating them. It’s about planning the entire system: which tasks should run locally, which should go to external models, how to distribute computing resources, which subscriptions make sense, and what hardware is actually worth buying.

We also need to plan our physical infrastructure — computers, servers, GPUs, memory, power consumption, and future expansion.

I believe local AI will become an essential part of our lives. It’s already making its way into smartphones, personal computers, and even experimental operating systems.

Four things that matter most

1. Real value. The number one priority is getting useful, practical results.
2. Cost. How much are we actually paying to complete a task — not just for hardware, subscriptions, or tokens?
3. Security. This deserves its own category, outside any ranking. Personal data, privacy, and control over our infrastructure require special attention.
4. Ease of use. How easily, quickly, and reliably can we get the job done?

These are the criteria I use to compare technologies and build my own setup.

Subscriptions and APIs: two worlds starting to merge

This is an interesting topic that often gets overlooked.

Cloud AI can be accessed through subscriptions with usage limits or through APIs where you pay for what you actually use.

Each approach has its own economics, limitations, and advantages. And they need to be evaluated separately.

But the line between them is starting to blur.

A recent example is OpenAI. Eligible ChatGPT Pro subscribers can receive $100 to $500 per month in API credits, depending on their subscription tier. These credits require separate activation, and API billing remains separate.

That’s a pretty interesting example of two pricing models coming together.

So I’m not just comparing models. I’m also exploring the economics of subscriptions and APIs, their limitations, and how to combine them efficiently.

Below: the three areas I’m actively researching, my hardware experiments, AI agents working together, and how I plan to make all this information accessible to you.

What I’m researching — and what’s coming next

My three main areas of research

1. Local AI servers

I’m experimenting with hardware starting at just $500, although I’d recommend around $1,000 for serious practical use.

I currently have access to systems worth up to $15,000, plus rented, far more expensive GPUs for testing models that wouldn’t make economic sense to run on my own hardware.

Raw model performance isn’t my main priority. Hardware and software are just tools.

What matters is what these systems can actually accomplish for businesses and homes, how reliably they work, and when local computing makes more financial sense than the cloud.

2. Cloud models and AI APIs

From cheap APIs, including Chinese models, to frontier AI. I’m comparing capabilities, limitations, real-world costs, and integration with local systems. Subscriptions versus pay-as-you-go APIs are another important factor — the most powerful model isn’t always the smartest economic choice.

3. AI agents

Beyond their obvious benefits, I’m particularly interested in collaboration between independent multi-agent ecosystems. My Hermes and Grokbot systems are already working together — two separate ecosystems, each with its own agents and tools.

AI meets the physical world

One of my biggest priorities is connecting AI to affordable smart homes, offices, servers, computers, and everyday devices.

I’m exploring how autonomous, secure, reliable, and affordable these systems can become — especially for individuals and small businesses.

AI coding is moving into the background

I’ll be focusing less on AI coding. Not because it matters less, but because it’s becoming just another tool.

The goal isn’t to write code. It’s to get things done.

I’m interested in complete, real-world workflows that AI can handle with minimal human involvement.

Maximum value, reasonable cost

I’m not trying to prove that local or cloud AI is better. Sometimes the right choice is a $1,000 PC, a $10,000 server, rented GPU time, a cheap API, or a frontier-model subscription.

What matters is the cost of the final result — not the tools used to achieve it.

What’s coming next

My posts have mostly been scattered updates from individual experiments, sometimes rough and unstructured. I want to turn them into a coherent, connected picture of AI infrastructure — hardware, models, agents, costs, and real results. Detailed technical specifications and diagnostics will be collected automatically and shared separately, likely through links.

I also plan to give you direct access to my AI agents. You’ll be able to ask them anything about my experiments, hardware, configurations, and results. They’ll answer using the collected data — and if they can’t, I’ll step in personally.

My goal is to build an open, organized, and genuinely useful picture of what AI can accomplish at different budgets.

I’m combining local and cloud AI, planning resources intelligently, and focusing on real results for businesses and homes.

That’s what I’m working on — and what I’ll keep sharing with you.

Don’t choose sides. Combine the best.

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