Open vs closed AI models: which should startups choose?

One of the first decisions for startups building AI features is which model to use. There are two basic options: closed models offered via API by major providers, and open-weight models you can download and run on your own infrastructure.

Closed (API) models

• Pros: usually the highest general performance, no setup, constant updates, scaling handled by the provider.
• Cons: per-use cost, data sent to the provider, dependence on the provider's pricing and terms, no access to model internals

Open-weight models

• Pros: keep data on your own infrastructure, freedom to customise and fine-tune, unit cost advantages at high volume, less vendor lock-in
• Cons: infrastructure and operations load (GPUs, scaling, security), performance that can lag the latest closed models, licence terms that need careful review

"Open" model licences differ; some restrict commercial use or use above certain scales. See our open-source licences guide.

Selection criteria

1. Task performance: test on your own data and target languages, including Turkish; see Turkish NLP.
2. Data sensitivity: in health or finance, where data is processed can be decisive; see AI and KVKK.
3. Volume and cost: APIs suit low volume; your own infrastructure can win at very high volume; see AI costs.
4. Team skills: hosting models needs ML and infrastructure expertise.

Hybrid approach

Many startups use a strong closed model for complex tasks and a small open model for simple, high-volume tasks. An architecture that makes switching easy keeps you flexible.

Conclusion

The right choice depends on the task, data sensitivity and scale. APIs for a fast start, open models for control and scale. To compare model quality, see our evaluation guide.