Open Source vs Closed Source AI Models: What’s the Difference

Ask ten UK businesses which AI model they use and you’ll get ten different answers. Some run ChatGPT through a browser tab. Others have quietly switched their whole workflow to Llama or Mistral running on servers they control themselves. The split between open and closed AI models isn’t a niche debate anymore. It’s shaping who controls the technology, who profits from it, and who carries the security risk when something goes wrong.

Most people never think about this choice. They just open ChatGPT and start typing. But for UK businesses building products on top of AI, the open-versus-closed decision affects cost, compliance, and how much control you actually have over your own tools. It’s a decision that quietly shapes everything downstream.

What Open Source AI Actually Means

Open source AI means the model’s weights — the billions of numbers that make it work — are published for anyone to download, inspect and run. Meta’s Llama family, Mistral’s models and DeepSeek’s releases all fall into this camp. Hugging Face, the main hub for these downloads, now hosts more than 1.5 million public models as of 2026.

That doesn’t mean every “open” model is fully open. Some publish weights but not training data. Others restrict commercial use above a certain company size. Read the license before you assume you’re free to build a product on top of it.

There’s also a difference between open weights and open source in the strict sense. A truly open model publishes the training data, the code, and the weights. Most “open” AI releases only give you the weights — enough to run the model, not enough to fully understand how it was built. That distinction matters if your business ever needs to prove exactly what data trained the system you rely on.

Closed Source: The ChatGPT and Claude Model

Closed source models — GPT-5.5, Claude, Gemini — never leave the vendor’s servers. You send a request through an API, get a response back, and the actual weights stay locked away. Nobody outside OpenAI, Anthropic or Google has ever seen inside these systems.

This approach dominates the consumer market. ChatGPT alone processes over a billion messages a day worldwide in 2026. The tradeoff is control: you’re renting intelligence, not owning it.

Vendors justify the secrecy on safety grounds — a model’s weights in the wrong hands can be stripped of safety training and misused. Critics counter that secrecy also protects a competitive advantage worth hundreds of billions in valuation. Both things can be true at once.

The Middle Ground: Open-Weight but Restricted

A growing category sits between the two extremes. Some vendors release weights publicly but attach usage restrictions, revenue-sharing clauses, or approval requirements for large-scale commercial deployment. Meta’s Llama license is the best-known example of this hybrid approach.

These “source-available” releases give researchers the transparency benefits of open weights without fully giving up commercial leverage. For most small UK businesses the practical effect is the same as fully open — you can download and run the model freely — but it’s worth knowing the category exists before you scale up usage.

Some open-weight models also restrict use in specific countries or for specific applications like military use. These clauses rarely affect ordinary commercial deployment, but a compliance team should still flag them during procurement, especially if the business plans to expand internationally.

Why the Distinction Matters for Security

Open weights can be audited line by line. Security researchers can check for backdoors, bias, or hidden behaviour before deployment. That transparency cuts both ways — the same open access lets bad actors strip safety training out entirely.

Closed models hide their internals, which limits independent audits. But the vendor also patches vulnerabilities centrally and fast. When I looked into how UK fintech security teams actually weigh this tradeoff, most said audit rights mattered less than contractual liability — who pays when the model gets it wrong.

Neither approach is inherently safer. It depends on whether your team has the expertise to audit code, or would rather outsource that risk entirely to a vendor with a support contract. Insurance is starting to catch up too — some UK cyber policies now ask specifically which category of model a business runs.

Cost Differences for UK Businesses

Running an open model yourself means paying for GPU hardware or cloud compute, not per-token API fees. A self-hosted Llama 4 setup can cost a UK startup roughly £400-£800 a month in cloud GPU rental for moderate usage.

Compare that to a heavy GPT-5.5 API user spending £2,000+ monthly on token costs alone. I’ve seen three London agencies switch from GPT-4o to self-hosted open models purely to cut that bill. Break-even usually arrives within four to six months.