What is the difference between open and closed AI models? A closed model is used only through the provider’s app or API, so you send your data to them and never see the model itself. An open-weight model can be downloaded and run on your own hardware, which gives you more control over data and customisation but also leaves you responsible for running, securing and updating it. Neither is better in general; the right choice depends on your data, budget and skills.
Behind this practical choice sits one of the biggest debates in global technology: whether powerful AI should be widely available or tightly held. The debate affects safety, competition, national strategies and, very concretely, what tools Vietnamese developers and businesses can use.
What do “open” and “closed” actually mean in AI?
A trained AI model is, at its core, a very large file of numbers called weights, plus the code needed to run it. A closed model keeps those weights private. You reach it through the company’s chat app or through an API, a paid connection that sends your text to their servers and returns an answer. Most of the best-known chat assistants work this way.
An open-weight model publishes the weights so that anyone can download them and run the model on their own computers or rented servers. Several large technology companies, startups and research groups in the United States, Europe and China release models this way, often in several sizes, from small ones that run on a laptop to large ones that need powerful servers.
“Open” is a spectrum, and the words are used loosely. Many models described as open source release the weights but not the training data or the full training code, and come with licences that restrict some uses or very large users. Purists argue that true open-source AI should disclose much more. When a company says “open”, it is worth reading the licence before building a business on it.
Closed does not mean secretive about everything, either. Closed providers often publish research, safety reports and documentation; they simply do not hand over the model. The practical difference for a user is who runs the model and where the data goes.
Open-weight vs closed models: how do they compare in practice?
The comparison below reflects general patterns rather than any specific product, and the gap between the two camps shifts as new models appear.
| Factor | Closed model via API | Open-weight model you host |
|---|---|---|
| Getting started | Minutes: sign up, get a key, send requests. | Days to weeks: choose a model, set up hardware or cloud servers, deploy and test. |
| Where data goes | To the provider, under its terms; business plans usually offer stronger protections. | Stays on your own servers or chosen cloud, under your control. |
| Upfront cost | Low; pay as you go. | Higher; GPUs or rented servers plus engineering time. |
| Ongoing cost | Grows with usage; can become large at high volume. | Mostly fixed infrastructure; can be cheaper at steady high volume. |
| Skills needed | Basic software integration. | Machine learning operations, security, monitoring and updates. |
| Customisation | Limited to what the provider allows, such as prompts and some fine-tuning options. | Full control: fine-tune on your data, change behaviour, run offline. |
| Top-end capability | Usually where the most capable models appear first. | Often close behind, and strong for many everyday tasks. |
| Vietnamese language | Generally good in large commercial models; test with your own texts. | Varies widely; some models are tuned specifically for Vietnamese. |
| Licence and risk | Governed by service terms that can change. | Governed by the model licence; some restrict commercial or large-scale use. |
Many organisations end up using both: a closed model for complex, low-volume tasks and an open-weight model for high-volume or sensitive work.
What are the arguments on each side of the debate?
Supporters of open models make three main arguments. First, openness lets researchers everywhere study, test and improve models, rather than trusting a few companies’ claims. Second, it supports competition and lowers costs, because anyone can build on a strong base model. Third, it gives countries and companies outside the United States and China a way to build AI in their own languages and keep data at home, often described as AI sovereignty.
Supporters of closed models, or of more caution with the most powerful systems, argue that once weights are released they cannot be recalled. Safety filters can be removed from an open model, and the model can be adapted for harmful uses such as large-scale scams or disinformation. With a closed model, the provider can monitor misuse, update safeguards and cut off abusive users.
Both sides have a point, and many experts take a middle position: openness is valuable for most models, while the most capable frontier systems deserve extra scrutiny before release. Governments are still working out how to handle this, and their approaches differ.
Geopolitics and models for the Vietnamese language
Geopolitics adds a twist. Strong open-weight models now come from several countries, including China. That expands choice for everyone, but also raises questions some organisations care about, such as which values and content rules are built into a model, and whether it suits regulated sectors. Testing a model on your own sensitive topics before deploying it is sensible regardless of where it comes from.
For Vietnam, open models are also how local language work advances. Vietnamese companies and research groups have released models tuned for Vietnamese, usually built on top of open foundations. Without open weights, that kind of local adaptation would be much harder.
How can a Vietnamese team choose between open and closed AI?
Answer these questions in order; most teams reach a clear decision by the fourth step.
- Define the task precisely. Drafting marketing text, answering customer questions, summarising contracts and analysing internal documents have very different needs for quality, privacy and volume.
- Classify the data involved. If the task uses personal data, health records or confidential contracts, weigh data control heavily, and check legal obligations with a lawyer.
- Test quality on your own examples. Run the same twenty to fifty real Vietnamese tasks through a few closed and open models, and have staff score the results blind.
- Estimate volume and cost honestly. Calculate expected monthly usage. At low volume, a closed API is almost always cheaper than running your own servers.
- Check your skills. Self-hosting needs someone who can deploy, secure, monitor and update models. Without that person, an open model can become a security risk.
- Read the licence and the terms. For open models, confirm commercial use is allowed for your size; for closed services, read data retention and training terms.
- Plan an exit. Design your system so you can switch models later. The landscape changes fast, and lock-in is a real cost.
Start small either way. A pilot on one well-defined task teaches more than months of comparing specification sheets.
Frequently asked questions
Is an open-weight model the same as open-source AI?
Not exactly. Open-weight means the trained weights are downloadable, but training data and code may stay private and licences may restrict use. Stricter definitions of open-source AI require more disclosure.
Are open models less safe?
They carry different risks: safety filters can be removed and the provider cannot monitor misuse, but openness also lets independent researchers find problems. For your own deployment, security depends heavily on how well you run it.
Can a small company run an AI model on its own computer?
Small open models can run on a powerful laptop or a single server with a good GPU, which is enough for some tasks. Larger models need more expensive hardware, so compare carefully with API costs.
Which is better for Vietnamese language tasks?
It depends on the model and the task. Large commercial models are generally strong, while some open models are tuned specifically for Vietnamese. Test both on your own texts before deciding.
What is AI sovereignty?
It is the idea that a country should be able to build, run and adapt AI in its own languages and keep sensitive data under its own control, rather than relying entirely on foreign providers. Open models make this more achievable.
