Open vs Closed AI Models: Which Should You Use?

You'll often hear about "open" and "closed" models. The difference affects cost, privacy, control and risk.

You access them through an app or API. The provider hosts and controls the model.

Pros

• Often the most capable.
• Easy to start with, and no hardware needed.
• Regular improvements and support.

Cons

• Your data goes to the provider (check their terms).
• Prices and behaviour can change.
• Less control and transparency.
• Vendor lock-in.

The trained model files can be downloaded and run on your own hardware or a host you choose.

Pros

• Privacy and control: data can stay in your environment.
• Customisation: you can fine-tune.
• Predictable costs at scale.
• Works offline.

Cons

• You manage setup, security and updates.
• May be less capable than the best closed models for hard tasks.
• Need suitable hardware.
• Licences vary.

• Some models are truly open source (permissive licences like Apache 2.0 or MIT).
• Many are open-weight with extra conditions: revenue caps, regional exclusions or non-commercial limits.
• Training data and code may not be shared.

Always read the licence before commercial use.

Prototype with a closed model, learn what you need, then test whether an open model meets the quality bar for the parts where privacy or cost matter.

Neither is universally better. Match the model to your data sensitivity, budget, quality needs and ability to manage it.