ChatGPT vs. Building Your Own: A Reality Check for London SMBs

“We should build our own AI system—then we’ll own the data and save money long-term.”

I hear this from London business owners almost weekly. The logic seems sound: why pay monthly subscriptions when you could host your own AI model?

After spending months experimenting with local AI models, I can give you the honest answer most consultants won’t: for SMBs, building your own is almost always a expensive mistake. This is one of the key reasons why many AI experiments fail.

Here’s what I learned from actually trying both approaches.

The Appeal of “Building Your Own”

The reasons SMBs consider hosting their own AI models make perfect sense:

Data Control: Keep sensitive business information on your own servers rather than sending it to external services.

Cost Savings: Avoid monthly subscriptions by running models on your own hardware.

Customization: Train models specifically for your industry and use cases.

Independence: No reliance on external services that could change pricing or disappear.

These benefits are real—in theory. The question is whether they’re achievable in practice for most London SMBs. Before even considering this path, you should ask yourself the right questions about AI implementation.

What Actually Happens When You Try

I decided to test local AI models for product management work, thinking I could create a personal assistant environment without relying on online services. The motivation was solid: control costs, keep data private, and avoid licensing restrictions.

The Technical Reality:

Setting up even a basic local model required about 1.5GB of disk space and 2GB of RAM—and that was for a relatively small model. To run something approaching ChatGPT’s capabilities, you’d need roughly 900GB of storage, 1TB of RAM, and powerful graphics cards.

The Performance Gap:

The local model I tested had a context window of about 2,000 tokens (roughly 5-6 pages of text). ChatGPT’s free version handles 8,000 tokens, with enterprise versions managing 128,000 tokens—that’s nearly 200 pages of context.

More importantly, the output quality was dramatically different. Where ChatGPT produces coherent, contextual responses, my local model often generated repetitive text that looked like this:

“The Moon is a natural resource that can be mined for use on Earth. The Moon is a natural resource that can be mined for use on Earth.” Repeated endlessly.

The Time Investment:

After months of experimentation, my conclusion was clear: “For the time it took me to explore this, local models are still not directly useful for busy product managers.”

That’s time I could have spent actually solving business problems rather than wrestling with technical infrastructure.

Why ChatGPT Usually Wins for SMBs

Conversation Quality: Commercial AI services like ChatGPT have extensive manual refinement and factual enrichment that gives you an “inflated impression of their raw ability.” The conversation layers that make these tools actually useful are a significant competitive advantage.

No Infrastructure Headaches: ChatGPT works immediately. No server setup, no model downloads, no troubleshooting hardware compatibility issues.

Continuous Improvement: Your ChatGPT subscription gets better automatically as the underlying models improve. Local models require manual updates and management.

Support and Reliability: When ChatGPT has issues, it’s OpenAI’s problem. When your local model fails, you’re troubleshooting alone at 2 AM.

The Middle Ground: RAG Systems

Before jumping to building your own AI models, there’s a practical middle option many London SMBs overlook: RAG (Retrieval-Augmented Generation) systems.

RAG lets you use powerful existing models like ChatGPT while connecting them to your specific business documents and data. Instead of training a model from scratch, you’re essentially giving ChatGPT access to your company knowledge base.

Why RAG Often Makes More Sense:

• Keep using proven AI models that actually work
• Incorporate your specific business information and processes
• Maintain some data control by hosting your own documents
• Much faster and cheaper to implement than building custom models
• Get results that are relevant to your business without the infrastructure headaches

This approach addresses the main reasons SMBs consider building their own systems (customization and data relevance) without the massive technical complexity.

When Building Your Own Might Make Sense

There are still legitimate scenarios where local AI models make business sense, but they’re rarer than most people think:

Highly Sensitive Data: If you’re handling data that absolutely cannot leave your premises, local models might be worth the complexity and cost.

Very High Volume: If you’re processing enormous amounts of data daily, the per-token costs of cloud services might eventually exceed local hosting costs.

Specific Compliance Requirements: Some industries have regulations that make external AI services impractical.

Technical Expertise Available: If you already have a team capable of managing AI infrastructure, the overhead is less significant.

Notice what’s not on this list: saving money, getting better results, or having more control over day-to-day operations. This aligns with our broader framework for understanding when to use RPA, AI, or just better processes.

The Hidden Costs of “Building Your Own”

Hardware: The server capacity needed for effective AI models represents significant upfront investment.

Expertise: Managing AI infrastructure requires specialized knowledge that most SMBs don’t have in-house.

Maintenance: Models need updates, servers need monitoring, and systems need troubleshooting.

Opportunity Cost: Every hour spent managing AI infrastructure is an hour not spent growing your business.

Performance Risk: Local models may not deliver the quality needed for customer-facing applications.

The Practical Decision Framework

For most London SMBs, the choice is straightforward:

Use existing services like ChatGPT when:

• You need reliable, consistent results
• Time-to-implementation matters
• You want to focus on business problems, not technical infrastructure
• Your data sensitivity allows for cloud processing

Consider building your own when:

• You have legitimate data sovereignty requirements
• Your usage volume makes cloud costs prohibitive
• You have existing technical expertise to manage the infrastructure
• You can afford the time and money investment upfront

Your Next Move

The businesses succeeding with AI aren’t those building the most sophisticated custom solutions. They’re the ones implementing practical AI tools that solve real problems quickly and cost-effectively. And yes, your competitors are probably already doing this.

Before considering building your own AI infrastructure, ask yourself: would this time and money be better spent on core business activities that actually generate revenue?

If you’re unsure whether existing AI services meet your needs, or if building your own genuinely makes sense for your situation, book a free consultation to review your specific requirements.

We’ll give you an honest assessment based on real implementation experience—including when building your own might actually be the right choice.

QVXX helps London SMBs make practical AI decisions. We focus on what actually works for your business, not what sounds impressive.