Open-source AI vs closed AI: what is the difference, and which one fits your business?
If you have ever used ChatGPT, you have used closed AI. The model runs on an American company’s servers, and you rent it: you pay for it, and you see exactly as much of it as the maker decides to show.
There is another world, though. Models anyone can download, run on their own machine, and even modify. For free.
That is open-source AI. By 2026 it has become one of the main front lines of the technology race, and more and more business owners ask us the same question: which one is right for us? Let me walk you through it in plain language.
What do open and closed actually mean?
An AI model, at its core, is one enormous file: billions of numbers that record everything the model “knows” from its training. Whoever has that file owns the model.
With closed models, that file is a trade secret. You can never download the models behind ChatGPT (OpenAI), Claude (Anthropic), or Gemini (Google): they run on the maker’s servers, and you reach them through an API. An API is a programming connection point: your software sends a question, the model sends back an answer, and the maker bills you for every single call.
With open models, the model file is published. You can download it, run it on your own servers, and you never have to report to anyone what you use it for. The Chinese DeepSeek, Meta’s Llama, and the French Mistral all work this way.
The fine print: “open” is not always open source
As a founder with a law degree, I cannot skip this clarification.
With most “open” models, only the finished model file is public (the industry calls these open-weights models); the training data and the training code are not. It is as if you were handed the finished cake, along with the right to reshape it. The recipe stays in the baker’s drawer.
Licenses vary widely too. DeepSeek R1, for example, was released under the MIT license, which grants real, almost unlimited freedom. Meta’s Llama 4, by contrast, uses its own “community license,” which the official definition of open source does not recognize, and which requires a separate permission for companies with more than 700 million users. That last part is unlikely to affect you, but it shows the point well: it is always worth looking behind the word “open.”
Pros and cons
| Aspect | Open model | Closed model |
|---|---|---|
| Cost | No license fee, but running it requires infrastructure | Usage-based pricing, predictable entry |
| Data privacy | Your data stays on your own servers | Your data goes to the provider’s servers |
| Capability | A few months behind the frontier models | The best quality available |
| Operations | The responsibility is yours: updates, security, GPUs | Zero operations, the provider’s problem |
| Customization | Freely fine-tunable | Only what the provider allows |
What does this mean for your company?
For most small and medium-sized businesses, a closed model is the right starting point. The quality per forint is unbeatable, there is nothing to operate, and a customer service chatbot or a proposal-writing assistant does not need its own AI infrastructure.
An open model comes into the picture in three situations.
The first is data sensitivity: if you work with data that must not leave your servers (health, legal, financial), an open model is the only solution that lets you guarantee that technically.
The second is volume: above a certain scale, a rented GPU can be cheaper than paying per call. A smaller open model already runs on a 16 GB gaming graphics card, and a serious rented GPU costs on the order of a few hundred thousand forints (≈EUR 1,000–2,500) a month. As a rule of thumb, it is only worth doing the math above several million processed tokens a month.
The third is compliance. The EU AI Act has imposed obligations on providers of general-purpose AI models since August 2, 2025, and genuinely open-source models that are not offered for money are exempt from some of them. We wrote a full guide to the EU AI Act for Hungarian SMEs about the details.
The AI race: how did this become geopolitics?
In January 2025, the Chinese company DeepSeek published its R1 model for free, and it competed with the best American models. US tech stocks lost a trillion dollars of value within days. The press has called it the DeepSeek moment ever since.
Since then, the open side has been dominated almost entirely by Chinese labs. DeepSeek released V4 in April. Alibaba’s Qwen family is now the most-downloaded model family in the world, ahead of Meta’s Llama. And just this week, Moonshot AI announced Kimi K3: with 2,800 billion parameters it will be the largest freely downloadable model of all time when the model file is published on July 27. According to the company’s own tests, it finishes right behind the best American closed models on several tasks.
The name GLM 5.2 is worth remembering too. Zhipu AI’s model, released in June, is an open model built specifically for programming, and on some measurements it performs at the level of its closed rivals. Downloading a programming AI for free would have been science fiction two years ago.
Why give it away? Partly because of the American chip restrictions: Chinese companies cannot compete on raw computing power, so they use open releases to collect users, feedback, and influence. The strategy works: a growing number of countries are building their national AI infrastructure on Chinese open models.
The US has been forced to respond: in August 2025, OpenAI released its first freely downloadable models since GPT-2. Europe approaches it from the direction of sovereignty: the French Mistral received a 1.7 billion euro investment to become the European alternative.
How big is the gap, and what comes next?
The numbers paint a surprisingly balanced picture. According to measurements by the research institute Epoch AI, the best open models trail the closed frontier models by four months on average. According to Stanford University’s annual AI report, the difference in general knowledge has practically disappeared, but it persists on the hardest tasks: on an autonomous programming benchmark, closed models score 88.6% and open ones 76.8%.
What should you expect in the coming years? That both worlds are here to stay. Closed models will hold the top, and open ones will deliver the same level a few months later, ever more cheaply. So the question is not which one wins. It is which one fits your specific task: rented top-shelf quality, or knowledge you hold in your own hands and shape as you like.
If you would like to think through which one fits your company’s processes, let us talk in a 30-minute consultation. We have built systems on closed APIs and on open models running on our clients’ own servers: you can read about our services here.
This article was published on the Andronia blog. Andronia helps Hungarian businesses grow with AI solutions: custom software, automation, and AI compliance. Read about our services here.
