AI Openness Terms Explained

JOHANNESBURG, 19 October 2026 — The artificial intelligence industry has a terminology problem. As technology companies compete to release increasingly capable AI systems, terms such as open source, open weight and proprietary are often used interchangeably, despite describing different levels of access, transparency and control.

For ordinary users, the differences may seem technical. For businesses, software developers and researchers, however, misunderstanding these terms can lead to costly decisions involving licensing, data security and technology investments.

The debate has become increasingly important as organisations look for alternatives to relying entirely on AI systems operated by major technology companies.

What Is Open-Source AI?

Open-source artificial intelligence refers to AI systems released with sufficient technical information and legal permissions for people to use, study, modify and share them.

The Open Source Initiative (OSI), which published its Open Source AI Definition 1.0 in October 2024, identifies four essential freedoms: using, studying, modifying and sharing an AI system.

Meeting the definition involves more than simply making an AI model available online.

Developers must provide access to important components, including model parameters, training and operating code, and sufficiently detailed information about the training data.

Importantly, the OSI definition does not require every original training-data file to be published. It requires enough information for a skilled person to understand the data and build a substantially equivalent system.

Examples identified by the OSI as meeting its requirements include EleutherAI’s Pythia and the Allen Institute for AI’s OLMo.

Open-source AI can allow researchers to examine how systems were developed, improve them and build alternative versions without depending entirely on the original provider.

What Does Open-Weight AI Mean?

Open-weight AI is different.

When developers train an AI model, the system learns numerical parameters known as weights.

These weights are a central part of how the model processes information and produces responses.

An open-weight release makes those trained parameters available for others to download.

Depending on the licence, users may be able to run the model on their own computers or servers, fine-tune it for specialised tasks and build applications around it.

However, the original training datasets, training code and development processes may remain private.

This means a company can distribute a functioning AI model without revealing exactly how it was created.

Meta’s Llama family provides a widely recognised example of models distributed with accessible weights under their own licensing conditions.

Open weights can offer substantial practical freedom, but that does not automatically make a system open source under the OSI definition.

What Is Proprietary AI?

Proprietary AI refers to systems whose developers retain control over important components, usage rights or access.

Users typically interact with these systems through websites, applications or application programming interfaces, commonly known as APIs.

The underlying model weights and training processes generally remain unavailable for independent inspection or modification.

Many commercial AI services operate under this model.

For example, the principal hosted models offered through ChatGPT and Anthropic’s Claude are proprietary systems.

Companies developing proprietary AI often argue that controlled access helps protect intellectual property, maintain security and fund expensive research and infrastructure.

Customers may also benefit from managed services, technical support and regular updates.

However, proprietary systems can create dependence on a provider’s pricing, availability, product decisions and contractual conditions.

It is also important to recognise that proprietary software does not always require cloud hosting. Some proprietary models and applications can run locally under restrictive licences.

Open Source Vs Open Weight Vs Proprietary AI

The differences become clearer when comparing what users can actually access.

Feature	Open source	Open weight	Proprietary
Model weights	Available	Available	Usually closed
Training code	Available	May be closed	Usually closed
Training-data information	Detailed disclosure required	Often limited	Usually limited
Independent modification	Permitted	Licence-dependent	Restricted
Redistribution	Permitted under licence	Licence-dependent	Usually restricted
Commercial use	Permitted under open-source terms	Licence-dependent	Contract-dependent
Local deployment	Generally possible	Often possible	Depends on product

These categories describe rights and technical access, not whether a model is accurate, safe or capable.

A proprietary model may outperform an open-source alternative on particular tasks, while an open model may offer greater flexibility or control.

Why The Word ‘Open’ Can Be Misleading

One of the industry’s most common misunderstandings is assuming that publicly available technology is automatically open source.

A company might allow anyone to download a model while imposing restrictions on commercial use, redistribution or particular applications.

Another company might publish research papers and model documentation but keep its weights private.

Neither arrangement automatically satisfies the OSI’s definition of open-source AI.

The term open model is also broader than open-source model.

It may describe systems that publish some technical components while withholding others.

The important question is not simply whether a model is described as open.

It is what has been released, what remains unavailable and what the licence permits users to do.

Why Businesses Should Understand The Difference

The choice between open-source, open-weight and proprietary AI can affect a company’s operating costs and control over sensitive information.

A business using a proprietary AI service may pay subscription fees or usage-based API charges.

That arrangement can be convenient because the provider manages much of the infrastructure.

An organisation using an open-weight model may instead host it on its own servers.

This can provide greater control over deployment, customisation and where information is processed.

However, running a model independently requires computing resources, technical expertise, maintenance and security controls.

Open-source AI may offer additional opportunities to inspect or modify the technology and reduce dependence on a single supplier.

None of these options is automatically the cheapest.

The appropriate choice depends on the organisation’s workload, technical capacity, licensing obligations and security requirements.

Does Open-Weight AI Protect Your Privacy?

Running an AI model locally can reduce the need to send information to an external provider.

For businesses handling confidential documents, financial information or customer records, this may be an important advantage.

However, local deployment does not automatically guarantee privacy or security.

An organisation must still protect its servers, restrict access, maintain software updates and comply with applicable data-protection laws.

In South Africa, organisations processing personal information must consider their obligations under the Protection of Personal Information Act (POPIA).

The deployment method is only one part of that responsibility.

A poorly secured local AI system may expose sensitive information, just as a badly configured cloud service can.

Is Open-Source AI Safer?

The relationship between openness and AI safety is complicated.

Open-source systems allow independent researchers to inspect components, test performance and investigate vulnerabilities.

This can improve transparency and encourage outside scrutiny.

At the same time, making powerful models widely available may allow them to be adapted for harmful purposes.

Proprietary systems offer developers greater control over access and deployment, but outsiders may have less information about how those systems were trained or evaluated.

Neither approach guarantees safety.

Independent testing, responsible deployment, documentation and appropriate oversight remain important regardless of the licensing model.

The debate over transparency is especially relevant as AI developers and independent evaluators discuss how much information about advanced systems should be publicly disclosed.

What About Free AI Models?

Another common misconception is that open source means free of cost.

Open-source licences generally permit use without requiring payment for the software itself.

But operating AI models can still involve substantial expenses.

Users may need powerful graphics processors, electricity, cloud infrastructure, storage and specialised technical support.

Similarly, some proprietary AI services offer free access while keeping their underlying models closed.

Price and openness are therefore separate considerations.

A free chatbot is not necessarily open source, and an open-source model is not necessarily inexpensive to operate.

Why This Matters For South Africa

The distinctions have practical implications for South African businesses, universities, startups and government departments.

Organisations developing AI tools for local languages, education, agriculture, healthcare or customer service may benefit from the ability to adapt models to specific needs.

Open-weight and open-source systems can provide opportunities to develop applications without relying entirely on foreign-hosted services.

They may also support research into African languages and locally relevant datasets.

However, the costs of computing infrastructure, access to technical skills and responsible data management remain significant challenges.

Proprietary services can offer an alternative for organisations that need advanced capabilities without operating their own infrastructure.

For South African institutions, the decision should consider performance, cost, data protection, language support and long-term flexibility.

Five Questions To Ask Before Choosing An AI Model

Before adopting an AI system, businesses and developers should establish:

1. Can the model weights be downloaded and operated independently?
2. Is the training and inference code available for inspection?
3. What does the licence permit regarding commercial use, modification and redistribution?
4. Where will business and customer information be processed and stored?
5. What are the full costs of deployment, maintenance, security and support?

These questions provide a more useful basis for comparing systems than marketing descriptions alone.

The Future Of AI Openness

As artificial intelligence becomes more important to the global economy, the distinction between different forms of openness is likely to remain central to debates about innovation, competition and accountability.

Some developers will continue releasing model weights and technical documentation to encourage adoption.

Others will prioritise proprietary systems that provide controlled access and managed services.

Fully open-source AI projects will continue offering opportunities for independent development and research.

The essential point is that openness exists across several dimensions.

A model can be publicly accessible but legally restricted. It can have downloadable weights without public training code. It can also provide substantial technical transparency while remaining expensive to operate.

Understanding these differences allows users and organisations to make better-informed decisions about the AI technologies they adopt.