Open vs Closed AI Models: Which Should You Use?

One of the first real decisions a business faces when adopting artificial intelligence is whether to use an open or a closed model. It sounds like a technical detail, but it shapes your costs, your control over your data, the effort required to run things, and how easily you can switch course later. Get it right and you build on solid ground. Get it wrong and you may overpay, lose flexibility, or take on work you did not need to.

This guide explains the open-versus-closed choice in plain language for decision-makers. We will define what each term means, lay out the genuine trade-offs without taking sides, and give you a clear way to decide which approach fits your situation. If you are weighing this alongside the broader question of which specific model to pick, our guide to choosing the right AI model is a natural next read, and our plain-English guide to artificial intelligence covers the fundamentals.

What open and closed actually mean

A closed model, sometimes called proprietary, is one that a company builds and keeps under its own control. You do not get to download it or see how it is built; instead you access it as a service over the internet, usually paying for what you use. The provider handles the infrastructure, updates, and maintenance. Most of the best-known AI assistants work this way.

An open model, more precisely an open-weight model, is one whose trained internals are made publicly available so that anyone can download, run, and adapt it. You can host it on your own infrastructure, inspect it, and modify it to your needs. It is worth noting that open here usually means open weights rather than fully open in every sense, but the practical point is that you can take the model and run it yourself rather than renting access. Both open and closed systems are built on the large general-purpose foundations described in our guide to foundation models.

Closing the gap
Leading open-weight models have narrowed much of the performance gap with the best closed models.
Source: Stanford HAI AI Index

The case for closed models

Closed models are the easiest way to get started, and for most businesses they are the sensible default. Because the provider runs everything, you need no specialist infrastructure or AI engineers; you simply connect to the service and begin. The leading closed models, such as OpenAI's GPT-5 family, Anthropic's Claude, and Google's Gemini, are typically at or near the cutting edge of capability, and the provider keeps them updated, secure, and available without any effort on your part.

The trade-offs are control and data. You are dependent on the provider's pricing, availability, and policies, and your data passes through their systems, so you must trust and verify how they handle it. Costs scale with usage, which is cheap to start but can grow at very high volume. For the great majority of organisations, the convenience and capability outweigh these concerns, especially in the early stages.

The case for open models

Open models appeal when control, privacy, or cost at scale become priorities. Because you can run an open model on your own infrastructure, your data need never leave your environment, which is valuable for sensitive or regulated information. You are not tied to one provider's prices or policies, you can customise the model deeply, and at very large volumes running your own model can work out cheaper than paying per use. Capable open-weight families such as Meta's Llama, DeepSeek, Alibaba's Qwen, and Z.AI's GLM have made this a realistic option.

The catch is responsibility. Running an open model yourself means providing the computing power, technical expertise, security, and ongoing maintenance that a closed provider would otherwise handle. For a business without technical staff this can be a significant burden. Open models give you freedom, but freedom comes with the work of looking after everything yourself.

Open versus closed models at a glance
Consideration Open vs closed
Ease of starting Closed is far simpler; open needs setup and expertise
Data control Open keeps data in-house; closed sends it to a provider
Maintenance Closed is handled for you; open is your responsibility
Cost pattern Closed pays per use; open shifts cost to infrastructure

How to decide which is right for you

Start with an honest look at your situation. If you are early in your AI journey, lack technical staff, and want results quickly, a closed model is almost always the right choice. The convenience, capability, and absence of infrastructure work let you focus on the business problem rather than the plumbing. The overwhelming majority of organisations should begin here, and many never need to move beyond it.

Consider an open model when specific pressures make it worthwhile: you handle highly sensitive data that cannot leave your environment, you operate at a scale where per-use costs have become significant, you need deep customisation, or you have a regulatory requirement for full control. Crucially, you also need the technical capability, in-house or through a partner, to run and maintain it. If those conditions do not apply, the extra effort rarely pays off.

Start closed
For most businesses, a managed closed model is the pragmatic first step before considering self-hosting.
Source: NIST AI Risk Management Framework

You do not have to choose only one

Many organisations end up using both. You might rely on a closed model for general work while running an open model for tasks involving particularly sensitive data. Designing your systems so you can switch or combine models keeps you flexible as your needs change and as the market evolves, which it does quickly. The goal is to avoid locking yourself permanently into a single approach before you understand your real requirements.

Whichever path you take, the same good practices apply. Be careful about what data you share, keep humans reviewing important outputs, and strengthen your data and analytics foundations so any model works from good information. For customer-facing uses, a ready-built solution like a WhatsApp AI chatbot abstracts the open-versus-closed decision away entirely, handling the model choice for you. If you would like help weighing these options for your business, explore our AI chatbot solution or get in touch with our team.

Frequently asked questions

Does "open" mean the AI model is free?+
Not exactly. The model itself can usually be downloaded without a per-use licence fee, but running it costs money in computing power, infrastructure, and maintenance. Open shifts the cost from paying a provider to running things yourself rather than removing cost altogether.
Are closed models always more capable than open ones?+
Not anymore. The strongest closed models often lead at the cutting edge, but leading open-weight models have closed much of the gap and are more than capable for most business tasks. For everyday work the difference is frequently small.
Which should a small business without technical staff choose?+
A closed model, in almost every case. Without the expertise to run and maintain an open model, the convenience of a managed service is well worth it. You can revisit the decision later if your scale or data needs change significantly.
Can I move from a closed model to an open one later?+
Yes, especially if you design your systems to avoid tight lock-in. Many businesses start with a closed model for speed and later adopt an open one for specific needs such as sensitive data or high volume, or run both side by side.

References

  1. Stanford HAI. "AI Index Report." hai.stanford.edu.
  2. NIST. "AI Risk Management Framework." nist.gov.
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