Open Source vs Closed AI Models: Which Should You Use?
The gap between open-weight and closed proprietary AI models keeps narrowing. Here's how to actually think through which type makes sense for a given use case.
Closed models from major labs are usually the most capable and easiest to start with; open-weight models offer more control, on-premises deployment and lower long-term cost for teams with the technical capacity to run them. Increasingly, the right answer for many organisations is both, used for different tasks.
The basic difference
A closed AI model — like the flagship offerings from most major frontier labs — is accessed through an API or app; you send it a request and get a response back, but you never see or run the underlying model weights yourself. An open-weight model publishes those underlying weights publicly, so anyone with sufficient hardware can download, run and even modify the model directly, without depending on the original developer’s servers at all.
The case for closed, proprietary models
Closed models from the largest labs are typically the most capable available at any given moment, backed by teams with the largest compute budgets and access to the newest hardware. They’re also the easiest way to get started — no infrastructure to manage, no hardware to provision, just an API key — which makes them the default choice for teams that want strong results quickly without building out AI infrastructure expertise in-house.
The case for open-weight models
Open-weight models offer something closed systems structurally can’t: full control. You can run them entirely on your own infrastructure, which matters enormously for organisations with strict data-residency or confidentiality requirements — sensitive data never has to leave your own servers. You can fine-tune them for a narrow domain, inspect exactly how they behave, and avoid being dependent on a single vendor’s pricing, availability or policy changes. For high-volume use cases, running your own infrastructure can also end up considerably cheaper than paying per-request API fees at scale.
The capability gap keeps narrowing
For years, the honest answer to “open or closed” leaned heavily toward closed models simply being meaningfully more capable. That gap has been narrowing steadily, with successive open-weight model releases closing in on frontier closed-model performance on many common benchmarks and real-world tasks. It’s part of why sectors with strong data-control incentives, like healthcare, have reported growing interest in open-source models specifically — the performance trade-off that used to make the decision easy is becoming less decisive every year.
How to actually decide
In practice, the choice increasingly isn’t binary. Many organisations now use closed, frontier models for their most demanding tasks — complex reasoning, cutting-edge coding — while running open-weight models for higher-volume, more routine tasks where the cost and control benefits outweigh a modest capability gap. If you’re just starting out, begin with a closed model’s free or low-cost tier to establish what’s actually useful for your work, then evaluate whether an open-weight alternative could handle the same job at lower ongoing cost once your usage patterns are clearer.