Why Some Businesses Are Skipping ChatGPT—and Building Their Own Local AI Instead

In the past year, nearly every business leader I’ve spoken to has tried ChatGPT, Gemini, or Copilot. Most are still experimenting. A few have made it stick. But a quiet shift is happening in the background—especially among firms with sensitive data, tight budgets, or a need for speed.

They’re building their own AI.

No, not from scratch.

They’re using open-source models like Llama 3.1 or Nemotron, running them locally on secure infrastructure, and tailoring them to specific tasks.

The goal?

Keep costs low, control data, and get results faster than the cloud allows.

Here’s why that matters—and what I’ve seen firsthand.

Why local AI is gaining traction

Most people assume AI means plugging into OpenAI or Microsoft. But that comes with trade-offs:

• Data risk: Even with protections, many organisations don’t want sensitive material leaving their network.
• Performance: Cloud-based tools can be slow, especially under load or with complex chains of reasoning.
• Cost: The more your team uses commercial AI tools, the higher the bill. These costs creep quickly.

Running a local model sidesteps all three.

With today’s open-source alternatives—like Llama 3.1, Mistral, or Nemotron—you can now fine-tune, deploy, and operate a model on-premises or on dedicated cloud resources. These models are smaller, cheaper to run, and increasingly competitive in quality.

And because they run locally, you get:

• Full control over the data and model
• Faster response times with no API bottlenecks
• Fixed infrastructure costs instead of usage-based surprises

That’s especially appealing to businesses in legal, healthcare, defence, finance, or research—anywhere privacy and performance aren’t negotiable.

What this looks like in practice

I worked with one client who wanted to automate complex research queries across thousands of academic papers. Initially, they used GPT-4. It worked—but it was slow, costly, and required constant prompt tweaking.

We switched to a fine-tuned local model running Llama 3.1 with a domain-specific retrieval chain. Not only did it speed up processing by 40%, but it also brought hosting costs down by 70%. The team could iterate faster, with full transparency on what the model was doing and why.

We’re seeing similar results across legal case search, compliance workflows, internal knowledge assistants, and even frontline agent support tools.

You don’t need a Data Science PhD—or a dev team

Here’s the part most people miss: this kind of deployment is now accessible to non-technical teams.

With modern no-code and low-code platforms—like LangChain, Flowise, or Dust—you can wire up a local reasoning model, plug it into your data, and deploy a custom tool in days, not months. Many of the hardest parts (like embeddings, vector search, or prompt orchestration) are now prebuilt into visual workflows.

You still need to understand the logic behind the model. But you don’t need to build infrastructure from scratch or write thousands of lines of Python.

So what’s the catch?

Like most thing AI - it is nunaced. Local models aren’t right for everything. They’re smaller, so they might not match GPT-4 on abstract creative tasks or general language fluency. But for reasoning within a known domain, they’re more than capable—and far more efficient.

The challenge is knowing where they fit in your business, how to tune them for your use case, and how to get your team using them properly.

What to do next

If you’re wondering how to reduce your AI spend, protect sensitive workflows, or speed up internal tools—this is the next step.

We help service businesses implement no-code and low-code AI solutions, including:

• Setting up local and open-source AI models securely
• Building custom tools that align with your workflows
• Training teams to manage and iterate without needing developers

If that’s something you want to explore, I’ll show you exactly where it fits—and how to test it, fast.

→ Book a No Code/Low Code AI Discovery Call

We’ll find the low-hanging fruit and show you how to go from experiment to impact—without overbuilding or overspending.